Ozzie Sport

This feels mostly finished in terms of content to add to it. I need to get feedback from my supervisors regarding what is missing or what could be improved. (I know it isn’t perfect. I just wanted it written so I could get on with editing it and realizing where the problems are.) Any and all feedback is appreciated.

Review of Literature

The review of literature will define what fans and sport fandom are, and examine how sport fans show allegiance to clubs they barrack for. The definitions of fandom and fans are key to understanding how and why people express interest in a team online. After that, the lit review will look at population studies of and characterizations of sport fandom in Australia.

The Definition of Sport Fandom

There are very few works that focus on the definition of fandom as it pertains to sport. Most literature presupposes that its readers understand what the concept means and then proceed to examine some aspect of sport fandom. While researching sports fandom and its definitions, four different groups were identified as offering their own perspective on how to identify and define fandom. The first group includes sports marketers and managers, who broadly speaking, define sports fandom around potential for spectatorship. The second group involves sports sociologists and historians; they tend to define sports fandom as a form of identity and as a product of a specific culture. The third group involves the media and sports fans, where the definition involves the expression of allegiance to a club that is often grounded in the moment or the short term. A fourth group was identified as doing research about sport fandom. This group includes popular culture studies academics, who tend to focus on fan interaction with the team and the game, and who focus on sport fandom as a smaller subcomponent of fandom.

Sports marketers and managers.

Stewart (1983) wrote for an audience of VFL fans, while providing a great deal of information regarding the organization of the league, its financial situation and other information that would be of more interest to people interested in sports marketing. Fans are frequently described based on their proximity to stadiums, training grounds and the location found in a team’s name. These descriptions were used to explain the potential for spectatorship: “The Club has little local support — there are few private dwellings in the vicinity — and most of its supporters are centred in the outer south-eastern suburbs.” (p. 41) In a few cases, fans are all described based on their economic status and how fans of other teams perceive them: “It is thought that while the affluent eastern suburbs residents are appreciative of the team’s success, they prefer to spend Saturday afternoon in active leisure activities like tennis or gardening.” (p. 40)

Shilbury, D., Quick, S., & Westerbeck, H. (2003) published a book about sports marketing. Much of the content is focused on fans from the perspective revenue for a club, league or sport converting them into spectators and consumers of merchandise related to the organization and its sponsors. The authors do not create their own definition of fan. Rather, the authors (p. 70) borrow from Smith and Stewart (1999) to define categories of fans: passionate fans, champ followers, reclusive partisans, theatergoers and aficionados.

Sullivan (2004) wrote for an audience of potential sport marketers. The author said “the term fan will be used in the broadest sense and will, therefore, imply a range of attachments.” (p. 131) Sullivan then characterized fans as spectators and consumers of various media who could be profiled using three key factors: Geographic, demographic and behavioral factors. The discussion around these factors involved how they impacted the potential for spectatorship and the consumption of media related to a team.

Nicholson (2004), a sport management academic, wrote to reflect on the problems the AFL faces in terms of becoming a national game. The geographic population imbalance between clubs in Queensland and New South Wales compared to Victoria was a major problem: The league was not balancing team location with population areas, nor was it financially sponsoring player development. The author rarely used the word fans to describe these problems: Spectators, the market and television audience are used instead.

Sports sociologists and historians.

Collins (2005) wrote for an audience interested in the history and evolution of several football codes in Australia. In describing a proposal to merge rugby league with Australian rules football, the different codes were described as appealing to two different views about Australia’s place in the world: transnational versus nationalistic. These differences point to a definition of sports fandom relating to identity.

Like Collins, Cashman (2002) was writing for an audience of those interested in history. Cashman (2002) differs in that his history focused on more on the connection between Australian sport and other issues in Australian life including identity, culture and parallel Australian history. Both authors connected sport to national identity. In the case of Cashman, the term fan is almost never used in the text. Words like crowds, interest, Australian with adjectives further identifying spectator culture and sport participants are used instead. Again, the author’s undefined use of fandom involves identity.

Adair, D. & Vamplew, W. (1997) are sports historians that tried to debunk some historical myths about sports culture in Australia. One of these myths involved the defining of Australians as sports obsessed. This definition, where fans are defined as people obsessed with sports, is reinforced by talking about match attendance compared to the total population, and by the consumption of sport on television. Spectatorship plays a role in the definition with less frequency than that of the identity of a nation obsessed with sports and as an important component of Australian popular culture.

Hess (2000) wrote specifically about the history of female fans of Australian rules football. In his text, he offered a topology of the female fan types. These included women as passive onlookers, voyeurs, socialites, barrackers, civilizers. Hess (2000) claims it is possible to “categorize female spectators on the basis of their behavior and their seating arrangements” (p. 127) but “it should also be noted that it was certainly acceptable for different social groups of women to be present at football games”. (p. 127) The author uses the term fan, spectator, supporter and barracker almost interchangeably. The definition that gets offered often ties into the type of fan to other types of identities such as class, social standing and occupation. Spectatorship plays a role in these definitions but often in the context of where female fans sit relative to those other identities.

Stewart (2005, p. 117) wrote for an audience Australian sport historians interested in the Australian Football League. In this work, for this audience, he referred to fans based on their sense of identity, their relationships with management and the behaviors they expressed. Like sport marketers, he too borrowed from Smith and Stewart (1999) to offer a paradigm for classifying fans. Stewart argues that there was a paradigm shift from away using the term barrackers and supports that occurred during the 1980s and 1990s. (Stewart, 2005, p. 115) The term fan was used to distinguish define the type of supporter and loyalty to the club. This type of issue had an impact on how Australian sport historian and sociologists used the term in their own research.

The media and sport fans.

The Canberra Times (2009, September 12) defines fandom based around personal identity of individual fans. They reference scholars who talk about supporting a club being similar to having religious experiences. The article goes on to talk about how modern fans express their identity. The actions they describe are fixed in short term expressions of allegiance: Buying and wearing a jersey, attending a match, crying or cheering depending on a club’s performance.

Monteverde (2010, October 11) wrote about the Harry Kewell’s fans in the Courier Mail (Brisbane). In the article, he talks about how fans responded to Kewell’s performance at two specific events: An A-League match and the World Cup, not on fan allegiance across Kewell’s career. Actions that were described were also temporal: Getting items signed, cheering for Kewell as he did things on pitch and applauding Kewell when he took the field.

The Gold Coast Bulletin (2010, October 9) described fans using a short-term indicator of Melbourne Heart fans getting heart when the Heart beat the Melbourne Victory. The Central Coast Express Advocate (2010, October also used fans in a context of an extremely limited time period when the newspaper talked about how fans had been promised that the pitch would be in good condition for an A-League match. Garvey (2010, October 10) in The Sunday Age had an article titled, ” ‘Rekindled’ does enough to keep his fans interested.” This title supports the idea that sport fandom is defined by newspapers around short term events.

Popular culture studies academics.

Popular culture studies scholars like Jenkins (2006) and Hills (2009) offer the last definition. They define fans as an active population who engage in activities related to an object produced by the larger popular culture. This production includes activities like writing fan fiction, creating costumes, producing fanvids and organizing conventions. The act of production goes helps in constructing a new identity. (Bennett, 2010) Popular culture academics also define fans as possessing a sense of ownership of their product that is removed and distinct from the official one, that they view actors, athletes, copyrighted and trademarked materials as communally owned by fans.

Sport fandom definition.

Almost all the definitions of sport fandom predate the rise and importance of the Internet. Those that come later often ignore the presence of sport fans online. In trying to determine what a sport fan is in the context of online fandom, bits and pieces need to be borrowed from the four definitional categories. In the context of this research, sport fandom is defined as the collective group of fans organized, formally or informally, around a particular sport, club or athlete where individual members identify as fans and express that allegiance in a way that can be observed by outsiders.

Population and Characteristics of Australian Sports Fandom

While there are many definitions and underlying assumptions as to what a fan is, there has been less work done looking at what makes these communities demographically distinct from populations. Much of the work done approaches the issue from the perspective of comparing the population of different sports or leagues. When specific clubs are looked at, the literature tends to focus less on research done about fans of those teams than it does on repeating traditional narratives about fan allegiances that may date back one hundred years.

Sports.

Delpy & Bosetti (1998) conducted a demographic study of sports fans online that found sports fans were 6% more likely to be female (36% compared to 30%) and were 1.3 years older (34 compared to 32.7) than the whole population of the Internet.

Adair, D. & Vamplew, W. (1997) cite a study that found that during the 1970s, 28 percent of men and 21 percent of women in Australia regularly attending sporting events as spectators.

The VFL and AFL.

The VFL and AFL have been attributed with having historically high levels of female fans, both as spectators and barrackers, when compared to other football codes in Australia and around the world. In the early days of the sport, female spectatorship was between 30 and 50 percent. (Cashman, 2002, p. 48) This contrasts with Australian, specifically New South Wales, rugby which is characterized as being conservative, middle class, patriarchal and often containing strains of misogyny that discouraged the growth of female spectatorship. (Cashman, 2002, p. 52) Both codes were characterized as having large white spectator bases. (Cashman, 2002, p. 56) For much of the history of Australian rules football, it was characterized as being a fundamental part of life in Melbourne; this began to change in the 1960s and 1970s as the game became more commercialized. (Stewart, 2005, p. 114)

Characteristics of sport fan communities can differ by club. A survey of research done about VFL/AFL teams reveals some of these different characteristics.

In 2001 and 2003, a nationwide survey was conducted of football fans to identify who they supported. (Stewart, 2005, p. 111) The Adelaide Crows respectively had 638,000 and 699,000 supporters. (Stewart, 2005, p. 111) (Roy Morgan Research, 2009, July 19). The 2009 team ranked sixth for total fans, with 629,000 fans. (Roy Morgan Research, 2009, July 19).

The Fitzroy Lions, who eventually became the Brisbane Lions, were originally from an area where their fanbase drew heavily from a population middle-class white-collar workers. (Shaw, 2006, p. 115) During the 1940s, Fitzroy Lions were similar to their counterparts at Collingwood and North Melbourne in terms of fan composition. Shaw (2006, p. 79) characterizes them as being drawn from the working classes and prone to violence similar to that of future British football hooligans. According to a 2001 and 2003 national survey, the Brisbane Lions respectively had 798,000 and 1,331,000 supporters. (Stewart, 2005, p. 111) (Roy Morgan Research, 2009, July 19). The 2009 team ranked second for total fans, with 861,000 fans. (Roy Morgan Research, 2009, July 19).

Early in the history of the Carlton Blues, most of their fans were from the Carlton area and represented the major population found there: “middle-class white-collar workers and the occasional silvertail.” (Shaw, 2006, p. 115) The Carlton Blues had one of the largest fan bases during the 1940s. According to Shaw (2006, p. 101), they could draw crowds irrespective of their on-field performance. According to a 2001 and 2003 national survey, the Carlton Blues respectively had 603,00 and 596,000 supporters. (Stewart, 2005, p. 111) (Roy Morgan Research, 2009, July 19). The 2009 team ranked seventh for total fans, with 493,000 fans. (Roy Morgan Research, 2009, July 19).

In the period around the Magpies founding in 1892, fans were characterized as being bootmakers and working in the footwear industry. (Grow, 1998, p. 69, 77) During the clubs early part of the 20^th century, the Collingwood Magpies fans were predominantly from Collingwood. They matched the characteristics of the neighborhood: Semi-skilled members of the working class that were mostly Irish Catholics. (Shaw, 2006, p. 115) The Collingwood Magpie fans are characterized as having “strong working-class origins”. (Stewart, 1983, p. 35) The club has historically enjoyed strong local support, both in terms of developing a fan base and with local businesses. During the early part of the 1900s, 70 percent of the club supporters were local and 80 percent were members of the working class. (Sandercock, 1981, p. 199) In the decade around 1900 to 1910, fans were described as being drawn from the working class. (Shaw, 2006, p. 79) During the 1940s, the club had one of the largest fan bases in terms of game attendance. According to Shaw (2006, p. 101), the team could draw crowds irrespective of their on-field performance. During the 1960s and 1970s, there was a geographic shift in the fanbase where the fanbase extended out to Melbourne’s north east suburbs. (Stewart, 2005, p. 113) There was a also demographic shift by the 1970s, with over 50 percent of the local population being not native born and Anglo-Irish-Australian; instead, the local fan base was composed largely of Southern Europeans. (Sandercock, 1981, p. 200) The characterization of working class values continued on despite these changes. According to a 2001 and 2003 national survey, the Collingwood Magpies respectively had 688,000 and 749,000 supporters. (Stewart, 2005, p. 111) (Roy Morgan Research, 2009, July 19). According to Roy Morgan Research. (2009, July 19), the modern team has the third largest AFL fan base, with over 731,000 fans.

During the 1870s and 1880s, Essendon was one of the three big clubs in terms of the number of paying fans. (Grow, 1998, p. 55) During the early part of the 20^th century, Essendon Bombers fans were drawn from the local area and fans were mostly from the lower middle-class. (Shaw, 2006, p. 116) Essendon Bomber fans are from the “moderately affluent north-west suburbs” who have a reputation “for being conservative and responsible.” (Stewart, 1983, p. 36) According to a 2001 and 2003 national survey, the Essendon Bombers respectively had 862,000 and 796,000 supporters. (Stewart, 2005, p. 111) (Roy Morgan Research, 2009, July 19). The team has the fourth largest AFL fan community in 2009 with 638,000 barrackers. (Roy Morgan Research, 2009, July 19).

According to a 2001 and 2003 national survey, the Fremantle Dockers respectively had 237,000 and 367,000 supporters. (Stewart, 2005, p. 111) The team has the eleventh largest AFL fan community in 2009 with 337,000 barrackers. (Roy Morgan Research, 2009, July 19).

During the 1870s and 1880s, Geelong was one of the three big clubs in terms of the number of paying fans. (Grow, 1998, p. 55) According to a 2001 and 2003 national survey, the Geelong Cats respectively had 357,000 and 345,000 supporters. (Stewart, 2005, p. 111) (Roy Morgan Research, 2009, July 19). The team has the eighth largest AFL fan community in 2009 with 488,000 barrackers. (Roy Morgan Research, 2009, July 19).

The Hawthorn Hawks fans are characterized as being from the affluent eastern suburbs, but who were not as interested in attending matches as fans of other teams. (Stewart, 1983, p. 40) According to a 2001 and 2003 national survey, the Hawthorn Hawks respectively had 362,000 and 390,000 supporters. (Stewart, 2005, p. 111) (Roy Morgan Research, 2009, July 19). The 2009 club ranked tenth in the AFL for most fans, with 381,000. (Roy Morgan Research, 2009, July 19).

The Melbourne Demons are characterized as not being able to draw local support, with most of the team barracking for the team being “centred in the outer south-eastern suburbs.” (Stewart, 1983, p. 41) According to a 2001 and 2003 national survey, the Melbourne Demons respectively had 226,000 and 205,000 supporters. (Stewart, 2005, p. 111) (Roy Morgan Research, 2009, July 19). The team had the fewest people barracking for them of any team in the AFL during the 2009 season; only 187,000 people identified themselves as fans in research conducted by Roy Morgan (2009, July 19).

The North Melbourne Kangaroos fans during the early part of the 20^th century are described as being from the working class and being a precursor of the British football hooligans. (Shaw, 2006, p. 79) During the 1900s and 1910s, many barrackers and players were butchers. (Fiddian, 1977, p. 132) During the 1920s, this occupation continued to compromise an important part of the team’s supporter base. (Shaw, 2006, p. 83) According to a 2001 and 2003 national survey, the North Melbourne Kangaroos respectively had 268,000 and 249,000 supporters. (Stewart, 2005, p. 111) The 2009 club ranked second to last in the AFL for most fans, with 219,000. (Roy Morgan Research, 2009, July 19).

Port Adelaide Power membership peaked in 1998 with 38,305 members. (Ruccie, 2010, October 7)According to a 2001 and 2003 national survey, the Port Adelaide Power respectively had 274,000 and 315,000 supporters. (Stewart, 2005, p. 111) (Roy Morgan Research, 2009, July 19). The 2009 club ranked thirteenth in the AFL for most fans, with 245,000. (Roy Morgan Research, 2009, July 19).

At the Richmond Tigers founding in 1885, fans were characterized as being larrikins who threatened the club’s existence by keeping away paying customers. (Grow, 1998, p. 72) During the early 20^th century, Richmond Tigers fans were mostly semi-skilled Irish Catholic members of the working class. (Shaw, 2006, p. 115) The Richmond Tigers supporters are characterized as “defiant and arrogant.” (Stewart, 1983, p. 42) Prior to the 1950s, being born in Richmond meant being a Richmond Tigers fan. This pattern of fans being located close to the historical home of the team changed with in the post war era. (Sandercock, 1981, p. 183) According to a 2001 and 2003 national survey, the Richmond Tigers respectively had 398,000 and 401,000 supporters. (Stewart, 2005, p. 111) The 2009 team ranked ninth for total fans, with 392,000 fans. (Roy Morgan Research, 2009, July 19).

During the 1870s and 1880s, South Melbourne was one of the three big clubs in terms of the number of paying fans. (Grow, 1998, p. 55) The South Melbourne Football Club, that eventually became the Sydney Swans, began with most of their supporters being aspirational members of the lower-middle-class. (Shaw, 2006, p. 116) During the 1930s, the club was considered a Catholic one. (Shaw, 2006, p. 116) According to a 2001 and 2003 national survey, the Sydney Swans respectively had 1,305,000 and 1,341,000 supporters. (Stewart, 2005, p. 111) During 2009, the club ranked first in the AFL with 1,217,000 fans. (Roy Morgan Research, 2009, July 19)

During the 1880s, St. Kilda fans were characterized as being stockbrokers. (Grow, 1998, p. 69) During the 1960s and 1970s, there was a geographic shift in the location of St. Kilda’s supporters where the fanbase extended into the southern bayside suburbs. (Stewart, 2005, p. 113) According to a 2001 and 2003 national survey, the St. Kilda Saints respectively had 321,000 and 282,000 supporters. (Stewart, 2005, p. 111) The 2009 team ranked twelth for total fans, with 311,000 fans. (Roy Morgan Research, 2009, July 19).

According to a 2001 and 2003 national survey, the West Coast Eagles respectively had 692,000 and 746,000 supporters. (Stewart, 2005, p. 111) (Roy Morgan Research, 2009, July 19) The 2009 club ranked fifth in the AFL for most fans, with 632,000. (Roy Morgan Research, 2009, July 19)

During the 1930s, when the Western Bulldogs were known as the Footscray Bulldogs, the team’s fanbase was extremely local to Footscray; much of this was owed to the fact that players came from the immediate area. (Kingston, 2005, p. 43) According to a 2001 and 2003 national survey, the Western Bulldogs respectively had 198,000 and 254,000 supporters. (Stewart, 2005, p. 111) The 2009 club ranked fourteenth in the AFL for most fans, with 226,000. (Roy Morgan Research, 2009, July 19)

Soccer.

The early history of soccer fandom in Australia was dominated by British expatriates, both as players, administrators and barrackers. (Thompson, 2003) British barracking continued on into the 1950s when their supporters began to dwindle in comparison to other ethnic tribes. (Moore & Jones, 1994).During most of the 1960s, 1970s, 1980s and 1990s, soccer fandom in Australia was dominated by ethnic tribalism with the major barracking groups being composed of Serbians, Croatians, Italian, Portuguese, Greeks and Macedonians. (Hallinan, Hughson & Burke, 2007) (Crawford, 2003) (Moore & Jones, 1994) Some of the barracking during that period involved regional and city pride in clubs. Much of this took place in Newcastle. (Hallinan, Hughson & Burke, 2007) The patterns of ethnic tribalism began to dissipate with the emergence of the A-League in 2004. (Hallinan, Hughson & Burke, 2007) Regional allegiances began to develop around clubs like the Melbourne Victory. (Hallinan, Hughson & Burke, 2007) Ethnic tribalism continued to exist for soccer clubs in Australia after the creation of the A-League but it was relegated to the regional club levels in cities like Melbourne, where it this fan support was much less obvious to outsiders. (Hallinan, Hughson & Burke, 2007)

Demonstrating Club Allegiance

In the AFL, fans have historically expressed their allegiance to their clubs in a variety of ways. Two of the key areas for expression have involved cheersquads (Andrews, 2005) and the production of fanzines. (Wilson, 2005) Beyond that, club supporters have their own unique ways of demonstrating club loyalty.

Cheersquads have played an important roll in Australian rules football and date back to the 1880s. Cheersquads would create giant paper banners with inspirational messages for players to run through. (Andrews, 2005) Cheersquads also waved giant floggers. (Andrews, 2005) These were eventually phased out and replaced with patties, which are giant pompons on a small stick. (Andrews, 2005, p. 88) The 1970s saw a decrease in the ability of cheersquads to make and display signs as they interfered with signage by advertisers at the park. (Andrews, 2005, p. 84) By the 1980s and 1990s, fan control of cheersquads had been severely diminished as clubs and the leagues exerted increasing influence over them. (Andrews, 2005, p. 83) Despite the heavy restrictions that were eventually placed on cheersquads, membership to them is still viewed as extremely important part of barracker identity for many fans.

Many other expressions of allegiances are easier to understand: Up until about thirty years ago, if the Collingwood Magpies performed poorly, The Sporting Globe no one would buy it. (Shaw, 2006, p. 117) The morale of the city of Geelong is said to be dependent on the club’s performance. (Shaw, 2006, p. 116) According to Stewart (2005, p. 128), Hawthorn Hawks fans were so opposed to a proposed merger with the North Melbourne Demons that some traveled from Tasmania for the merger meeting.

According to the Canberra Times (2009, September 12), demonstrating allegiance to a rugby club by wearing a team jersey is a relatively new expression. Prior to this change, wearing a jersey and not being on the pitch was rather taboo. This demonstration of allegiance came into being as a club’s fanbase decentralized and was less structured around a specific geographic area.

The Connection between Sport Fandom and Online Activity

In defining sport and looking at how fans express allegiance in an Australian sport context, much of the research predates or ignores the online fan community. While the connection between online activity and sport is not clearly defined in the literature about Australian sport, it can be simply defined: According to Favorito (2007), the internet is a platform that allows “casual fans to connect with their individual favorite athlete more regularly.” The observation by Favorito, writing for people who are working with individual athlete, can also be used in application to clubs, leagues, sport in general and with other fans. Online activity and sport fandom are connected by this desire to connect to, and demonstrate allegiance to athletes, clubs, leagues and the sport by an attempt to connect with those groups and other fans of those groups.

References

Adair, D. & Vamplew, W. (1997). Sport in Australian History. Melbourne: Oxford University Press.

Andrews, A. (2005). “Taming the Cheersquads.” In M. Nicholson (Ed.), Fanfare, Spectator Culture and Australian Rules Football, Australian Society for Sports History (pp. 83-98). Melbourne: Australian Society for Sports History.

Bennett, C. (2010). Flaming the fans: Shame and the aesthetics of queer fandom in todd haynes’s velvet goldmine. Cinema Journal 49(2), 17-39. Retrieved August 18, 2010, from Project MUSE database.

Canberra Times. (2009, September 12). Canberra Times: Wild ride for sports’ biggest fans. Canberra Times (Australia) n.pag. Retrieved October 10, 2010, from NewsBank on-line database (Australia’s Newspapers)

Cashman, R. (2002). Sport in the National Imagination, Australian sport in the Federation decades. Sydney: Walla Walla Press.

Central Coast Express Advocate . (2010, October 8). Fans promised `exceptional’ surface. Central Coast Express Advocate (Sydney, Australia) (1 – Main Book ed.), 071. Retrieved October 10, 2010, from NewsBank on-line database (Australia’s Newspapers)

Collins, T. (2005). “‘One Common Code of Football for Australia!’: The Australian Rules and Rugby League Merger Proposal of 1933.” In R. Hess, M. Nicholson, & B. Stewart (Eds.), Football Fever: Crossing Boundaries (pp. 27-38). Hawthorn: Maribyrnong Press.

Crawford, D. (2003). Report of the Independent Soccer Review Committee into the Structure, Governance and Management of Soccer in Australia (Independent Soccer Review). Canberra: Australian Sports Commission.

Depley, L. and Boetti, H.A. (1998). “Sport Management and Marketing via the World Wide Web”, Sport Marketing Quarterly, 7 (1), pp. 21-27.

Favorito, J. (2007). Sports Publicity: A Practical Approach. Sport Management in Practice. New York City: Butterworth-Heinemann.

Fiddian, M. (1977). The Pioneers, 100 Years of Association Football. Abbotsford, Victoria: York Press.

Garvey, A. (2010, October 10). ‘Rekindled’ does enough to keep his fans interested – First Past The Post – Racing. Sunday Age, The (Melbourne, Australia) (First ed.), 16. Retrieved October 10, 2010, from NewsBank on-line database (Australia’s Newspapers)

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Grow, R. (1998). The Victorian Football Association in Control, 1877-1896. In R. Hess & B. Stewart (Eds.), More Than a Game (pp. 45-85). Melbourne: Melbourne University Press.

Hallinan, C. J., Hughson, J. E., & Burke, M. (2007). Supporting the ‘World Game’ in Australia: A Case Study of Fandom at National and Club Level. In S. Brown (Ed.), Football Fans Around the World, From Supporters to Fanatics, Sports in the Global Society (pp. 121-135). London: Routledge.

Hess, R. (2000). ‘Ladies are Specially Invited’: Women in the Culture of Australian Rules Football. In J. Mangan & J. Nauright (Eds.), Sport in Australasian Society, Sport in the Global Society (pp. 111-141). London: Frank Cass.

Hills, M. (2009). From BBC radio personality to online audience personae: the relevance of fan studies to Terry Wogan and the TOGs. Radio Journal: International Studies in Broadcast & Audio Media, 7(1), 67-88. doi:10.1386/rajo.7.1.67/1.

Jenkins, H. (2006) Convergence Culture: Where Old and New Media Collide. New York: New York University Press.

Kingston, R. (2005). “Football, Community and Identity in Melbourne in the 1930s.” In M. Nicholson (Ed.), Fanfare, Spectator Culture and Australian Rules Football, Australian Society for Sports History (pp. 41-52). Melbourne: Australian Society for Sports History.

Monteverde, M. (2010, October 11). What’s all the fuss about? – Quiet game, but Harry’s fans don’t care. Courier Mail, The (Brisbane, Australia) (1 – First with the news ed.), 072. Retrieved October 10, 2010, from NewsBank on-line database (Australia’s Newspapers)

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Shilbury, D., Quick, S., & Westerbeck, H. (2003). Strategic sport marketing (2nd ed.). Crows Nest, New South Wales: Allen & Unwin.

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Stewart, B. (2005). Channeling Passion or Manufacturing Identity? Managing fans in the Australian Football League. In M. Nicholson (Ed.), Fanfare, Spectator Culture and Australian Rules Football, Australian Society for Sports History (pp. 109-124). Melbourne: Australian Society for Sports History.

Sullivan, M. (2004). “Sports Marketing.” In J. Beech & S. Chadwick (Eds.), The Business of Sport Management (pp. 128-153). Essex: Pearson Education Limited.

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Related Posts:

• Most popular Australian (and New Zealand) sport related Twitter accounts (November 24)
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I’m currently toying around with the idea of creating a large Australian sport wiki. I’ve had a lot of data for a while to allow me to do this. Some of the data I have includes:

• A list of Australian sport teams.  List is about 3,000 teams long.  It often contains some other information like when teams were founded, team colors, etc.  This can be combined with a list of Australian sport teams on Twitter and Facebook.
• A list of Australian sport venues.  This can be merged with a list of Australian sport venues on Gowalla and Foursquare.
• A list of Australian athletes on Twitter and Facebook.  This is probably about 100 large.
• A list of Australian sport organizations on Twitter and Facebook.  This is probably about 50 large.
• A list of Australian sport fans on Twitter, Facebook, LiveJournal and its clones, bebo, orkut, Wikipedia.  This list is probably close to about 20,000 long.

This data could easily be supplemented with the following information that would be easily mineable:

• A list of AFL, NRL and other Australian professional athletes sorted by team.
• A list of other Australian athletes.
• A list of Australian sport organizations.
• A list of Australian sport journalists and broadcasters.
• A list of Australian sport websites.

This list could be further supplemented by:

• Importing Australian sport pictures from Flickr.
• Importing Australian sport videos from YouTube.

The quality of the wiki wouldn’t necessarily be all that high.  Most of the initial content would be stub.  Still, if I had a bot programmer and I was willing to devote a month to the project, it would be reasonably comprehensive in terms of initial content for others to use to build from.  It would possibly help in terms of developing a large idea of the scope of sport in Australia by giving an idea as to the size of the fan community, the depth of the interest in various sports, the number of teams, when those teams were created and folded, where sports are more popular than others.  (Though that all depends on the data mining that goes into the initial content.)  What keeps me from doing this is the fact that I feel like it might be a time sink of a month to create this content and I’m not sure if the time sink is worth the effort.  Basically, I need external motivation.

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My dissertation topic involves doing a demographic and geographic study of Australian sport fandom online. There are several sites and social networks where you can get publicly available demographic data to begin to formulate a picture of the user population, and then segment that population out by interest in a league, sport and athlete. I’ve spent a lot of time looking at Twitter, Facebook and LiveJournal. Recently, partly because of a trip to the Wikimedia Foundation and discussions with a few people at UCNISS, my interest in who was contributing to Australian sport wiki articles on Wikipedia increased.

Finding out who edited Wikipedia articles using publicly available information is a bit of a challenge. The most reliable information for who edited comes from IP address information. IP addresses can provide an idea as to the geographic location of the contributor. It is easy enough, with the help of a friend, to create a tool that pull the history of a Wikipedia article, get a list of IP addresses that edited the article, feed the IP address into another tool that will pull up the general location of the contributor. (One of my favorite visualizations of this type of information is WikipediaVision.) The data isn’t always accurate and if I was looking primarily at New Zealand, a country without its own dedicated IP address range, this would be even less reliable. Still, for my purposes, this data works pretty well.

This data is still pretty limited. There are a lot of articles that are edited by non-anonymous users. Sometimes, it is possible to get demographic and geographic information about Wikipedia contributors by viewing their profile pages. This can just be time consuming to do manually if an article has a large number of contributors as you need to view a lot of user pages. It becomes a deterrence for trying to collect geographic information about article contributors.

I was looking for a more time effective and accurate method of collecting geographic and demographic information about contributors that is publicly available on their user pages. The easiest and quickest way to get this information on a mass scale is to utilize user box information. Many user boxes, when included on a user page, put the user into a category. These categories are often then linked through the Wikipedian category structure. Beyond that, user boxes involve templates. It is easy to get a list of articles (user pages) that the template is included on.

The methodology that I selected from this point is rather straightforward. It involved:

1. Select a category.
2. Copy and paste the list of articles (user pages) in the selected category to an Excel spreadsheet. Sort the list alphabetically. Copy and paste only the user pages to Notepad. Replace * User with blank. Copy and paste this list back to Excel.
3. Create a filter where the cell contains / . Select those cells. Copy them to notepad, replace / with [tab] in order to remove user subpages from the list. Copy this back to Excel. Select only the column with usernames.
4. Run an advance filter in order to remove all duplicate rows.
5. Copy this list back to the dedicated spreadsheet. Label all those users with the category from which they were pulled in a unique column.
6. Repeat steps 1 to 5 until all the categories that you want to have included are included.
7. Merge/Group all the rows by username.

This method may not be the most efficient way of going about doing this. It can probably be improved by automating some of these steps. In my case, step 7 was not able to be completed using Excel. I had to e-mail the file to @woganmay, who I believe converted the file to a mySQL database, used the group feature, converted the results back to csv and e-mailed the file to me.

In my case, I did not complete this for every category. Some categories did not seem worth it time wise as they had too few user pages to be included. In other cases, the categories were just too big to do. This included all the members of User de, User en, User es, User fr, User it, User jp. Only a selected number of categories were included because of time constraints. Data gathering was focused on categories that I perceived would have the greatest number of Australians and other possible contributors to Australian related articles. When these categories were more exhausted, categories with between 1,00 and 5,000 articles were selected.

There are all sort of limitations to this data. First, not everyone includes userboxes on their profile pages. This means that there could be a lot more Australians on Wikipedia than indicated by userbox inclusion on a user page. The assumption for the resulting data is that proportional representation exists for various categories. So while there are X amount of Christians and Y amount of Atheists, the assumption that the relationship between X and Y will always be proportional to the actual population on Wikipedia. Whatever data is available thus has to be viewed as good enough or supplemented by going to individual user ages to see if other information is available when a user appears where no information for someone when running against the history of the article.

Second, even when they do exist, there are often useful pieces of information that are missing. For example, in an Australian context, there is a userbox for Rugby League fans. There is not however a userbox for Australian rules footy fans. There are also not user boxes and categories for fans of NRL or AFL teams. (This type of user box and category exists for National Hockey League teams.)

About halfway through this process, I realized that this data could be useful for analysis beyond who is editing Wikipedia. At the moment, I’ve only totaled data I have for Australians. It is pretty fascinating and would be neat to go further with: How does the proportional size of the Australian Wikipedian population compare against the actual population? Does the size of the Australian Atheist versus Christiah community actively reflect the proportions in Australian society? Or is the Australian Wikipedian community demographically distinct from the greater population?

The following tables include the data based on people who were included in Wikipedians in Australia and its subcategories and Australian Wikipedians. A copy of the raw data can be found at October 9 – Wikipedia English Data – Australians.xls. The data is provided without comment though any attempts at explaining the patterns found are very much appreciated.

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I’m pretty much done with Twitter.  By that I mean that I’ve compiled my list of Twitter accounts.  I’ve got a user geographic list.  Neither one is likely to be improved.  All I need to do now is run those accounts through my program, then count up where people are from, map that and write my dissertation chapter.  I intended to do that soon.  After I finish my Literature Review.

In the mean time, I’m playing with Wikipedia.  I’m interested to know who exactly is editing Australian sport articles.  I can do that some what easily with anon-IP address edits.  When it comes to logged in users, that’s a bit harder.  I need to be able to get information off contributor user pages to determine that.  I don’t know who edited what.  To make my life simpler, what I did was go category like Wikipedians by location, and put that information into an Excel spread sheet.  Various categories were combined.  (I don’t have all categories of Wikipedians by…  I have a random selection of larger categories.)

I’ve got some technical issues in combining rows on Excel.  (I don’t know how to do it.  I’m waiting for a friend to get on MSN so I can harass them to do that for me.)  Still, in the process of doing this…  I figured I would post what I had found so far.

I’ve identified 2,220 people who claim to live in Australia.  The tables below give an idea as to the current count of Australians that fall into these categories.  If you don’t see a category, don’t assume it means there are zero people in it.  It could mean that or it could mean I haven’t processed that row.  It could also mean that I didn’t include that category yet.  It could mean that a category for it doesn’t exist on Wikipedia.  (Example: I have yet to see a category for Aussie rules football fans.)

Another thing to bear in mind, this data is from userboxes.  A contributor has to put a userbox on their user page.  Not everyone does that.  Some people may put userboxes up for some categories like language but not others like alma mater or religion.

That said, findings so far:

So if you’re an Aussie Wikipedian, you likely went to ANU, have a BS, are a current university student, are an Atheist, of English descent, work as an engineer, speak Greek in addition to English, are a Rugby league fan, are a Wikipedia administrator and rollbacker and use IRC.

Need this complete to see how well this stands up.

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This feels mostly finished in terms of content to add to it. I need to get feedback from my supervisors regarding what is missing or what could be improved. (I know it isn’t perfect. I just wanted it written so I could get on with editing it and realizing where the problems are.) Any and all feedback is appreciated.

Methodology

Types of Social Media Research

When conducting social media research, there are ten general methods that can be used to gather and analyze data. These are:

1. Individual case studies for how a business uses social media and the web;
2. Search and traffic analytics analysis;
3. Sentiment analysis and reputation management;
4. Content analysis;
5. Usability studies;
6. Interaction and collaboration analysis;
7. Relationship analysis to try to determine how people interact and to identify key influencers;
8. Population/demographic studies;
9. Online target analysis of behavior and psychographics; and
10. Predictive analysis.

Each of these methods offers insights into various aspects of the web and its population. The type of analysis used is often specific to the purpose of the research, involved blended approaches from traditional analysis types, and different methods are often used in conjunction with each other. These methods often blend quantitative and qualitative analysis. Choosing the correct method of gathering analyzing data can be one of the biggest hurdles for being able to measure ROI and understand how a community works.

This section will provide a brief summary of each type, explain how to conduct this type of research and give examples that used that methodology.

Individual case studies for how a business uses social media and the web.

Case studies on social media usage are often done to measure the effectiveness of specific actions taken by an organization.

Bronwyn et al. (2005) say case studies “typically examine the interplay of all variables in order to provide as complete an understanding of an event or situation as possible. This type of comprehensive understanding is arrived at through a process known as thick description, which involves an in-depth description of the entity being evaluated, the circumstances under which it is used, the characteristics of the people involved in it, and the nature of the community in which it is located.”

This methodology often incorporates components of all the other methods discussed in this section. The specific methods often depend on the goals of the person or organization conducting the case study.

Vincenzini (2010) did a case study regarding the use of the social media by the NBA in an attempt to define why they have been successful in using it to promote the league. The author used quantitative analysis to measure the size of the community, the volume of content they were viewing on sites like YouTube and the volume of content they were creating on sites like Twitter. The quantitative analysis was synthesized with explanations from NBA employees to explain their practices in the context of their own business decisions as they pertained to social media. This was followed up with an explanation as to what worked and what did not worked and offered advise for others involved with sport and social media to help them leverage their own position.

Case studies are a mixed methodology approach, borrowing from other approaches. The major difference is that the case study focuses on a narrower perspective with the goal of tracking behavioral changes, or in advising others on how an organization changed practices and how those lessons can be applied elsewhere.

Search and traffic analytics analysis.

Search engine and traffic analytics generally is done internally to determine how to optimize a site in order to increase the amount of visitors a site gets and the total number of pages that they view. This method involves identifying how people arrive at a specific site and the pages they visit while at the site. Traffic analytics analysis often includes six different components: Search engine visitors, paid search advertisements, pay per click, organic traffic, direct traffic and internal site traffic.

Ramos and Cota (2009) define traffic analytics as “Tools that analyze and compare customer activity in order to make business decisions and increase sales. Analytics tools can report the number of conversions, the keywords that brought conversions, the sites that sent converting traffic, conversion by campaign, and so on.”

There are a number of different methods and tools that allow for this type of analysis. Early in the history of the Internet, one of the most popular tools and methods involved analyzing web server log files. (Jansen 2009) Another popular early method of analysis was page tagging, which involved embedding an invisible image on a page, which, when the image is triggered, “triggers JavaScript to send information about the page and the user back to a remote server.” (Jansen 2009) These earlier tools have advanced a bit and now include tools like Quantcast and Google Analytics. Kaushik (2010) recommends Google Analytics, a free tool that involves putting a bit of code on all pages of a site. Kaushik (2010) points out that various types of traffic analysis can be done using the various tools provided by Google Analytics. The author claims that Google Analytics allows you to break the analysis down into “three important pieces: campaign response, website behavior, and business outcomes.” (Kaushik 2010)

Fang (2007) completed a case study at the Rutgers-Newark Law Library in order to track library website usage, track visitor behavior and determine how to improve the website to better serve users. Earlier work done by the library had involved surveys handed out to patrons, analysis of log files, and the use of counters. (Fang 2007) The author changed methods because of some inherent flaws in using those methods to analyze website needs. They used Google Analytics in order to track user activity on the library’s website. The library “found out how many users were accurately following the path we had designed to reach a target page.” (Fang 2007) This sort of path following navigation was one of the goals they had when they designed their site. They also found out that “Visitor Segmentation showed that 83% of visitors were coming from the United States. About 50% of U.S. visitors were from New Jersey, and 76% of these were from Belleville and Newark. These results matched our predictions for patrons’ geographical patterns.” (Fang 2007) The results of this analysis enabled the library to make changes to improve their website.

This type of methodology lends itself more to a case study approach and often requires the consent of the website involved in order to get private logs. It can be used in conjunction with other methods, but should be used in a more targeted analysis of highly specific research areas.

Sentiment analysis and reputation management.

Sentiment analysis involves identifying content related to a topic and identifying the emotion connected to that content. In a sport context, sentiment analysis could involve determining if newspapers are providing positive or negative coverage of a team. In a social media context, sentiment analysis would involve determining attitudes being expressed on Twitter in individual tweets. Reputation management goes one further: After sentiment has been determined, a decision needs to be made on if and how negative and positive sentiment content should be responded to. Sentiment analysis is passive analysis where non-stakeholders can conduct analysis. Reputation management is active analysis that is primarily conducted by stakeholders as part of on going activities to improve a brand, be it personal or corporate.

While sentiment analysis and reputation management are similar in their desire to monitor a response to a situation, the tools available vary differently for each type. There a variety of different tools for sentiment analysis. One of the tools for conducting sentiment analysis are freely available lists of words “that evoke positive or negative associations.” (Wanner et al. 2009) Sterne (2010) suggests that content being ReTweeted on Twitter can be seen as a tool to measure positive sentiment. Sterne (2010) suggests that the ratio of follows/followers is not an effective tool for measuring sentiment on Twitter. Reputation management tools include Trackur. It allows you to “set up searchers and the system automatically monitors the Web for key words that appear on news sites, blogs, and other social media.” (Weber 2009)

Wanner et al. (2009) did a sentiment analysis of RSS feeds that focused on the 2008 United States presidential elections. They selected 50 feeds connected to the elections and collected updates to these feeds every 30 minutes for one-month starting 9 October 2008. For each item they collected off the feeds, they also recorded the date, title, description and feed id. (Wanner et al. 2009) After that, they eliminated all noise, which mostly consisted of non-content like URLS. (Wanner et al. 2009) The next step was to filter out all items that did not contain one of the following terms: “Obama”, “McCain”, “Biden”, “Palin”, “Democrat” and “Republican”. (Wanner et al. 2009) Sentiment was then analyzed using freely available lists “that evoke positive or negative associations.” (Wanner et al. 2009) The results were then visualized. Five events that happened during this period were chosen for a more detailed visual examination. They found that the news regarding possible abuse of power by Sarah Palin in Alaska resulted in many negative posts. They also found that the debates resulted in low sentiment scores for both candidates as the candidates attacked each other. The authors concluded that the visual tool they created would be useful for monitoring public debates.

This methodology can overlap with influencer identification (Weber 2009) as part of reputation management involves determining which people are worth responding to. It can also overlap with psychographics. Despite the obvious overlaps, this type of research often appears independently and not as part of a larger study.

Content analysis.

Content analysis involves looking at the individual components of something larger and analyzing it. In a social media context, the content could be comments on a Facebook fanpage, or all the tweets made by a person or group. Content analysis can be either qualitative or quantitative, depending on the purpose of the research.

With content analysis, the researcher views data as “data as representation not of physical events but of texts, images and expressions that are created to be seen, read, interpreted, and acted on for their meanings, and therefore be analyzed with such uses in mind.” (Krippendorff 2007) Krippendorff (2007) defines the basic methodology used in content analysis as unitizing, sampling, recording, reducing, inferring, and narrating.

An example of content analysis is a 2009 study by Kian, Mondello, & Vincent. It looked at ESPN and CBS’s internet coverage of men and women’s NCAA basketball tournament, also called March Madness. The methodology was spelled out by the authors as: “All 249 (N D 249) byline articles from CBS SportsLine and ESPN Internet were read, coded, and content analyzed to determine the descriptors in Internet articles.” (Kian, E., Mondello, M., & Vincent, J., 2009) The authors used multiple coders to help prevent bias in terms of interpretation of gendered language. The two sites in the sample were chosen because they were the largest. All types of March Madness content was included. Only the text of the content was included. Titles and authors were not. Categories for encoding gendered language were based on previous research by sport media researchers. Only descriptors were used for encoding. Totals for gendered descriptors were then calculated and an analysis was completed.

This method of analysis is can be done with other forms of analysis like sentiment analysis, as part of a usability study or collaboration study. It can also be done separately. It often appears most successful when done separately as part of a larger study to help provide context for other data analysis.

Usability studies.

In a social media context, usability studies look at how people use some aspect of the Internet or software that connects to it.

According to Sweeney, Dorey and MacLellan (2006), one of the purposes of a usability study is “point out specific usability problems with your Web site interface in line with how well your Web site speaks to your audience and their goals.” Jerz (2002) cautions that “Simply gathering opinions is not usability testing — you must arrange an experiment that measures a subject’s ability to use your document.” That caution also explains the general methodology of a usability study outlined by Jerz (2002): Collect both quantitative and qualitative data. The quantitative should involve some type of measurement. The qualitative should allow testers to express their opinions. Jerz (2002) suggests that you use at least five tests for the first run. Then, after fixing errors and problems based on tester feedback, you get another five testers to test the site to determine that those errors have been fixed.

An example of a usability study is one conducted by Sturgil, A., Pierce, R., & Wang, Y. (2010). The study tried to determine how much content readers of Internet news sites really wanted. The methodology involved conducting a focus group, and think-aloud sessions. In both cases, the researchers observed participants using Internet news sites. They also asked them questions regarding what content they visited and why. The methodology relied heavily on qualitative analysis with a small quantitative component.

Usability studies can be done in conjunction with traffic analysis and search analytics as the purposes are often similar: Improve the user experience and try to get users to complete certain tasks.

Interaction and collaboration analysis.

Interaction and collaboration analysis focuses on how people work together in an online environment. Collaboration analysis often looks more at how people work together to create something, such as contributing to a wiki or to create an event like an unconference, where everyone is working towards a common goal. Interaction analysis tends to focus on how people engage each other when there is no common goal in the group.

Software Services, Dale Carnegie & Associates, Inc., & Shedletsky, L. (2000) explain the methodology for interaction analysis. They encourage researchers to look at topics discussed, purposes of individuals’ utterances, structure of conversation, and how properties of talk affect outcomes when completing an interaction analysis. The researcher should determine the setting for which this type of analysis will be conducted: In a controlled setting such as a laboratory, by selecting samples of existing conversations, or by examining all conversation that the research is capable of overhearing. The researcher needs to determine if they will use prompted or unprompted interaction. They also need to determine how they will record conversations and if their analysis will be quantitative or qualitative in nature. Once these things have been determined, then a methodology for data collection can be figured out.

Viégas et al. (2007) did a collaboration analysis focusing on Wikipedia. The purpose of their work was to examine historical editing patterns and how editing practices have evolved over time. They built on work done by Viégas, Wattenberg and Dave in 2003. The methodology they used involved getting the editing history of articles across several different Wikipedia namespaces. The history of the articles was then examined using several visualization tools, metrics and methods depending on the established cultural practices for that namespace. One tool they used was a history flow visualization application. A method they used was the manual classification of “all user posts in a purposeful sample”. (Viégas et al., 2007) Metrics they used included count of horizontal rules, signed user names, new indentations levels, votes in polls and total “references to internal Wikipedia resources.” (Viégas et al., 2007) These tools, metrics and methods allowed them to examine how collaboration and interaction had changed over time.

This type of analysis often stands alone. It could be used as part of a usability study or relationship analysis to provide context for the results of those analysis types.

Relationship analysis.

Relationship analysis involves examining the relationships between users on a social network, message board or mailing list. The goal is to identify cliques of different sizes or people who are particularly influential in a particular group online. This type of analysis is important to many brands including Starbucks (Plimsoll, 2010). The purpose of relationship analysis is to identify key influencers and social who influencers who are or who have the potential to be brand evangelists. (Plimsoll, 2010)

Lord and Singh (2010) define social media influence marketing as being “about recognizing, accounting and tapping into the fact that as your potential consumer makes a purchasing decision, he or she is being influenced by different circles of people through conversations with them, both online and off.”

The methodology for influence identification is not clearly spelled out as identifying influencers can be heavily dependent on the network being examined and how the community on a specific site functions. As a result, social media marketers suggest an array of tools like Twitalyzer that can be used to help determine your own influence. (Ankeny 2009) Twitalyzer’s Peterson and Katz (2010) explain their site-specific method of determining influence as including the following variables: Engagement level, total followers, total following, hashtags cited, lists included on, frequency of updates, references by others, references of others, times content is retweeted, urls cited and a number of other variables. Sterne (2010) suggests using WeFollow.com to find people who use topic specific #hashtags on Twitter. The people who tweet the most about a topic are likely to be influencers in that others looking for tweets around a topic are likely to read them. In a wider web context, Sterne (2010) suggests using Technorati to identify bloggers who have clout and influence around a certain topic.

This type of research can be viewed as a fundamental component to sentiment analysis; social media marketing companies like Razorfish often package the two together. (Lord & Singh, 2010)

Population /Demographic studies.

Population studies involve defining the demographic characteristics of a community. In a population study, the goal is also to define the limits and size of the community that is being studied. Because of the complexity in defining the boundaries of a population and in sampling the whole of it, this type of research is rarely done in terms of social media.

Daugherty and Kammeyer (1995) define a population study as the assembling “of numerical data on the sizes of populations.” This sort of data is defined by the authors as “descriptive demographic statistics.” Daugherty and Kammeyer (1995) say “population numbers are always changing, so even if they are accurate when gathered they are soon out of date and inaccurate.” Daugherty and Kammeyer (1995) say the basic purpose of conducting a demographic study “is to explain or predict changes or variations in the population variables or characteristics.” Given the definition of a population study, the methodology involves counting all members of a select population.

The most famous example of a population study is the census. In the United States, this is done every ten years. According to the U.S. Census Bureau (n.d), the goal of the 2010 US census is ” to count all U.S. residents—citizens and non-citizens alike.” This is done by sending all citizens a ten-question questionnaire, requiring that people complete it by law and having a census taker follow up for all households did not return completed questionnaires. (U.S. Census Bureau, n.d.) The results are then calculated and are used by the government to make decisions.

This type of research often stands on its own. The results will often be utilized for marketing purposes in conducting other research, such as psychographics, to make that that sampling contains representative populations.

Online target analysis of behavior and psychographics.

Online targeting of and marketing towards a specific audience because of their demographic characteristics is extremely common on the Internet. Psychographics is a term that includes targeting towards a specific demographic group except it includes the offline component.

Sutherland and Canwell (2004) define psychographics as “market research and market segmentation technique used to measure lifestyles and to develop lifestyle classifications.” (p. 247) Nicolas (2009) defines online behaviorial analysis as a series of steps: Collecting user data across several sites, organizing information about users based on the sites they visit and their behavior on those sites, “infer demographics and interest data”, and classifying new users based on the collected data in order to deliver relevant ads and content based their demographic profiles. Kinney, McDaniel, and DeGaris (2008) define psychographics as attitude towards something such as a brand or involvement with an organization.

Given the methodology involved, much of this type of research involves action research in that it is done in a specific content, based on internal models to address specific situations.

An example of this type of research was done by Kinney, McDaniel, and DeGaris (2008) who investigated the demographic characteristics of NASCAR fans and their attitudes towards NASCAR, its sponsors and sponsor involvement with NASCAR. The research found that age, gender and education were all important variables in determining sponsor recall: Younger, more educated males had the best brand recall amongst NASCAR fans.

This type of research can be viewed as a subcomponent of a population study in that demographic information is sought about the population. In an online context, it often works in conjunction with search and traffic analytics analysis, content analysis, and interaction and collaboration analysis.

Predictive analysis.

A search on 13 July 2010 on SPORTDiscus had three results for “predictive analysis.” A search on the same date on Scopus had 605 results, 275 of which were in engineering, 132 in computer science and 102 in medicine. Predictive analysis is probably one of the least used analysis methods, especially in social media and fandom.

What is predictive analysis? At its simplest, it is identifying a future event or events, monitoring selection actions that precede the event and seeing if those events can be used to predict the outcome of similar events in the future. If a predictive value is found, an organization can monitor behaviors to help make more informed decisions.

An example of this type of research is “Predicting the Future With Social Media” by Asur and Huberman (2010). Their goal was to determine if tweet volume and sentiment on Twitter prior to a movie being released could be used to predict how well a movie performs at the box office. Their methodology involved identifying movie wider release dates that took place on a Friday, creating a list of keyword searches related to those movies, and using the Twitter API to collect all tweets and aggregate date that mention those keywords over a three month time period. The authors then compared the tweet volume to box office performance. They concluded that social media “can be used to build a powerful model for predicting movie box-office revenue.” (Asur & Huberman, 2010)

This type of research can be used in conjunction with other methods. It can be used along side a population study to see if certain actions will result in demographic changes.

Rational for Population Study

The literature review provides insight into the lack general quantitative analysis regarding the demographic and geographic characteristics of Australian sport fans in general and AFL fans in specific. Much of what is written involves observations based on match attendance, attendance statistics, common historical tropes based on the experience of the authors as members of the sport community or analysis based on demographic data around the community for which a club was based. The methodology rarely is spelled out. There is little reason to doubt the demographic composition of fans because most accounts match very well and there are a variety of citations that refer to a wide variety of sources. The literature review demonstrates a lack of quantitative research in terms of population characteristics.

The research regarding fan demographics in the Australian sport online community is even sparser. The focus on research being done tends to focus on fan production, such as the transition from fanzines to online mailing lists. It is often not quantitative in nature.

Given the hole in the research, there is a clear need to fill it to better understand the existing population of AFL fans who are increasingly using the Internet in order to facilitate their love of their chosen club. To do this, an appropriate methodology needs to be chosen. The previous section examined the major methodological approaches available for conducting research into social media and online populations. Most of these methods involve some form of interaction analysis or textual analysis. They do not offer a clear method of understanding the characteristics of a large group and its subcomponents.

Methodological Approach

The methodology used in this study will be a population study. To provide context for the findings, other methods will be utilized. The exact method for conducting the population study will differ depending on the site being examined. Therefore, most of the methodology used in this study will appear inside specific chapters.

Throughout this study, there is a dependence on user listed locations to determine the geographic location of members of the Australian sport fan community. To provide consistency across all sites looked at, a list was developed that included user generated location, city, state and country. The city, state and country were determined based on intelligent guesswork. For example, as the focus of the research is Australia, if Melbourne was standing alone, the assumption was that the user meant Melbourne, Victoria, Australia and not Melbourne, Florida, United States. Spelling variations and nicknames were also used to determine location. For example, Brisvegas is a nickname for Brisbane, Queensland, Australia. Often patterns of cities were looked for assuming the standard pattern of city, state, country or country, state, city or city, country or city, state. To aid in processing location lists more quickly, when using an automated tool such as the one for Twitter, the user-generated list was supplemented with one created by the author. This list included all Australian cities listed using the patterns of postal code and city, state and city, country, and city, state, country. The completed list contains over 65,000 variants that were listed by the author or user created.

References

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I’m having another period of annoyance with Twitter. I really feel like I should probably turn off alerts for followers again because right now? I’m pretty much putting people on a spammer list if they have 2,000 people they already follow. I’m also sending out cranky DMs blasting people for doing this sort of following.

For the past two months, I’ve spent a lot of time looking at Twitter. I’ve looked at follower counts. I’ve looked at follower geographic patterns. I’ve looked at people’s descriptions. I’ve looked at people’s geographic locations. The point of this is often to determine the geographic location of Australian sport fandom. I’ve read a fair bit on technology blogs about Twitter to help further my own understanding of Twitter to help me with intended mini-literature review in my Twitter chapter of my dissertation. I’ve basically been ODing on Twitter. There is a lot of interesting stuff out there.

But as a user? I’m getting pretty cranky. Seriously cranky. Every day, it feels like I’m getting 2 to 10 follows (across about 3 different accounts) from people who I don’t know, who are not geographically close to where I’m writing about, who don’t appear interested in professional sports, who have low interaction rates, who have 2,000+ people they follow. As part of my research, I constantly ask: What is the ROI for a team on Twitter in terms of where their audience is located? How can they best leverage their network? What can they provide for their fans to induce them to follow them? How can their fans help them? As a user, I can’t see how the people like I describe who follow me gain any benefit from that. (They can’t read me. I can barely keep up with 350. I function more or less because Americans get neglected as they post while I sleep.) (In one case, I got followed and unfollowed by about 5 times by the same user with 4,000 followers. ) As a user, I can’t see what they offer me. They rarely bother to explain.

And this is killing my desire to stay on Twitter. Seriously killing my desire to stay on Twitter. I just can’t. There are people I want and need to keep track of on Twitter for professional reasons. (The personal ones are almost exclusively on Facebook these days. On that level, I don’t feel the need to stay.) If you’re not active on Twitter and you cover social media, people sometimes doubt your legitimacy because you’re not using the product you’re discussing.

What I’d really like is for Twitter to make the following reforms:

1. Add a field for follow philosophy. It can be selecting from a list. It can be freeform writing. This way, when people follow others, they can see if they have a mutual philosophy. “I follow back people everyone.” “I follow friends, family and professional acquaintances.” “I follow celebrities.” “I follow only people with less than 1,000 followers.”
2. Allow people to block people with certain follow totals unless you follow them first. (I want to block anyone with 1,500 people they follow from following me first. If you want to follow me, interact with me first. Otherwise, add me to a list.) This way, spam following by power users is cut back.

The two following methods would help to kill off the Twitter spam following (and yes, your unwanted e-mail notification that you followed me to never read me is spam. It is unwanted and unsolicited and you didn’t indicate any mutual interest.) and help prevent my own fatigue. I use and prefer Facebook more than Twitter precisely because I’m not inundated with unwanted announcements like that.

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This is an updated version of Most popular Australian athletes, clubs, leagues and sport organizations on Twitter (version 2). If I’m missing anyone or anything, let me know.

The top 10 are unchanged since the last time I ran this. Stephanie Rice gained one place, going from 12 to 11. RealBigDell moved from 13 to 12. LionsRugbyTeam dropped from 11 to 13.  harry_o moved from 19 to 15. NRL and SamBurgess8 both dropped one. jarrydhayne_1 moved from 17 to 19. karmichaelhunt and jimstynes swapped places with jimstynes going up one and karmichaelhunt going down one. Of the top 20, warne888 gained the most total new followers with 15,055. LionsRugbyTeam gained the least with 144. harry_o, who had the biggest gain, had 2304 new followers. The biggest overall loser is BENJI_MARSHALL6 who lost 33 followers.

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• I'm at Wikimedia Foundation (149 New Montgomery, San Francisco): #
• I'm at Wikimedia Foundation (149 New Montgomery, San Francisco): #
• I'm at San Francisco International Airport (SFO) (1 S McDonnell Rd, at S Link Rd, San Francisco) w/ 45 others. 4sq.com/MTivk #
• I am reading Traffic: Why We Drive the Way We Do #Traffic bit.ly/ceSiAA #
• Back in Australia. Had great time at WMF. Learned lots. Excited to be back home. #
• Library toilet stall wall… twitpic.com/2tgtiv #
• I earned the Check-in Pro sticker on @GetGlue! bit.ly/aaMSne #
• One of my favorite tools changed and I can't use it anymore. Need a new tool for batch reverse geocoding with output to table for excel. #
• Got sport books. More cricket books than other sports here… (@ Vinnies) 4sq.com/ahUNhm #
• Quick question: Which one is better? Virgin or Qantas for short haul Canberra to Melbourne? Qantas is $30 more. #
• Went Virgin Blue. #
• Anyone in Melbourne willing to put me up for a few days around the 21 to 25 Oct? Poor student needs help. #
• Id #
• I earned the Triathlon sticker on @GetGlue! bit.ly/dy23gJ #
• Pictures from WikiProject Screencast: bit.ly/aTMqE5 #

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Related Posts:

• Most popular female Australian (and New Zealand) sport related Twitter accounts (November 24)
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 24)
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• Most popular female Australian (and New Zealand) sport related Twitter accounts (November 18)