Twitter-Based BIRGing: Big Data Analysis of English National Team Fans During the 2018 FIFA World Cup

Abstract

Sports fans tend to associate themselves with a successful team (BIRGing), while disassociating themselves with unsuccessful teams (CORFing). This premise was applied to social media commentary within England’s matches against Croatia and Colombia during the 2018 Fédération Internationale de Football Association World Cup, uncovering that English fans tended to perform Basking in Reflected Glory (BIRG) when England was leading or victorious and tended to engage in Cutting Off Reflected Failure (CORF) when England was trailing or defeated. In Method 1, team identification, national identification with England, and sentiment were significantly higher when England was leading or victorious than when they were trailing or defeated. In Method 2, machine learning generated trending graphs to detect that English fans BIRGed when they scored against Colombia; they also BIRGed more frequently during the match with Croatia, peaking several times when they scored a goal, saved a goal, or took a free kick. However, even though CORFing (i.e., lower team identification, lower national identification, and lower sentiment) occurred when the opposing team scored, English fans still BIRGed when they were finally defeated by Croatia, likely a function of the stage the game took place (World Cup semifinal), indicating that England had nonetheless succeeded in the World Cup as a whole.

Video kills the sentiment—Exploring fans’ reception of the video assistant referee in the English premier league using Twitter data

“…However, Yu and Wang [15] found that if fans tweeted about games for which they (supposedly) had no rooting interest, they expressed positive emotions more frequently than negative ones. In accordance with these findings, the patterns for the small number of games investigated by Fan et al [16] showed a similar relationship between the number of tweets and the share of positive sentiment. This also applies to findings from questionnaire-based surveys that show that people post on social media to express their emotions in general, but not to express particularly negative or positive emotions [24].…”

Light Gradient Boosting Machine for General Sentiment Classification on Short Texts: A Comparative Evaluation

“…TF-IDF was the only textual feature used to train the LGBM. Fan et al [41] built a sentiment model for recognizing the opinions (i.e. positive, neutral, and negative) of English national team fans during FIFA World Cup 2018.…”

What Drives Our Emotions When We Watch Sporting Events? An ESM Study on the Affective Experience of German Spectators During the 2018 FIFA World Cup

“…Therefore, we could not examine any unique effects of the changed competition mode in the knock-out stage or the affective dynamics of potential extra time and penalty shootouts. Moreover, despite converging evidence from other countries and sports (Cialdini et al, 1976;Fan, Billings, Zhu, & Yu, 2019;Hirt et al, 1992;Jones et al, 2012;Kerr et al, 2005;Leach & Spears, 2009;Snyder et al, 1986;Wann et al, 1994) as the study was confined to a German cultural context it is unclear to which extent the findings may generalise to other countries that (1) have a weaker soccer culture and (2) are not expected to do well. In order to address this question, future research should aim for a cross-cultural replication e.g., through a multi-national ESM-study during the 2020 UEFA European Championship.…”