The average American user is increasingly integrating politics into social identities, adding political words to describe themselves; they are more likely to describe themselves by their political affiliation than their religious one

Using Twitter Bios to Measure Changes in Self-Identity: Are Americans Defining Themselves More Politically Over Time? Nick Rogers; Jason J. Jones. Journal of Social Computing, Volume 2, Issue 1, March 2021), DOI 10.23919/JSC.2021.0002

Abstract: Are Americans weaving their political views more tightly into the fabric of their self-identity over time? If so, then we might expect partisan disagreements to continue becoming more emotional, tribal, and intractable. Much recent scholarship has speculated that this politicization of Americans' identity is occurring, but there has been little compelling attempt to quantify the phenomenon, largely because the concept of identity is notoriously difficult to measure. We introduce here a methodology, Longitudinal Online Profile Sampling (LOPS), which affords quantifiable insights into the way individuals amend their identity over time. Using this method, we analyze millions of “bios” on the microblogging site Twitter over a 4-year span, and conclude that the average American user is increasingly integrating politics into their social identity. Americans on the site are adding political words to their bios at a higher rate than any other category of words we measured, and are now more likely to describe themselves by their political affiliation than their religious affiliation. The data suggest that this is due to both cohort and individual-level effects.

6 Discussion and Conclusion

To the extent that a person’s Twitter bio is a valid measure of their sense of identity, Americans are defining themselves more saliently by their politics. This is important, because the formation of a group identity tends to change individual behavior in powerful ways. Through the phenomenon of “group polarization”, people who begin with vague, weakly-held opinions tend to become more radical and dogmatic when put into like-minded groups. They also quickly develop hostile feelings towards outgroup members. Rational, evidence-based dissent tends to lose effectiveness within the groups, and in fact make group members even more invested in their original opinion. To what may this increase in prevalence of political group identity be attributed? Is a more politicallyengaged set of people joining Twitter for the first time, making the aggregate site more political than it was in prior years? Or are existing Twitter users amending their profiles to add a political keyword where formerly there were none? In other words, is this a generational/cohort effect, or is change occurring within individual identities? As our data reveal: both. Comparison between the cross-sectional and the longitudinal data suggests that (1) new entrants are more politically-oriented than the older participants they are joining or replacing, and also (2) individual people are amending their identities to be more political. This dual nature of the phenomenon, as well as the effects it is likely to produce, portend a national polarization that is more likely to deepen than subside, in the short term. As Americans define themselves increasingly by their political allegiances, their feelings towards political “others” can be expected to become more negative, and debate on matters of policy will become more emotional and intractable. Traditionally, a solution to the problem of tribalism has been found in the concept of “superordinate goals”. Rival groups can put aside their perceived zerosum differences when presented with a shared obstacle that requires cooperation to surmount. In the Robbers’ Cave experiment, the Rattlers and Eagles were able to work together and even form intergroup friendships, once they were presented with obstacles that required cooperation for shared benefit[37]. Particular to our political context, some experimental research has suggested that priming a national identity (American) can mitigate partisan bias[38]. The attacks of September 11, 2011, for example, led to a period of bipartisan focus on international terrorism. Yet in the current political climate, such agreed-upon goals seem rare. Democrats and Republicans seem to diagnose distinct social maladies from each other, unable to even agree on shared definitions of problems.

Limitations and future inquiry.

Although we believe our method provides a useful, digital-age measure of individual identity that is similar to the seminal Twenty Statements Test, there are imperfections worth noting. First is the potential influence of “bots”. It is wellestablished that Russian intelligence sought in 2016, and continues to seek, to influence American political discourse through the creation of social media accounts that pose as American users and spread divisive (and often fabricated) political content[39]. It is conceivable that our documented increase in prevalence of political keywords in bios is partially attributable to a growing number of these bots. However, our best evidence suggests any such influence is minimal. To investigate this possibility we tested random subsamples of our data using “Botometer”, an automated tool to detect automated “bot” accounts. Almost all accounts received low scores. The mean for accounts in the longitudinal sample was 0.6 on a scale of 0 (probably not a bot) to 5 (probably a bot). The growth rate of botlike accounts fluctuated across our study period and could not account for the increases in political identity reported here. A full account of this analysis is included as the Appendix. A second concern: Are our findings generalizable to the American general public, or is the politicization specific to Twitter users? To be sure, a sample of Twitter users is not the same as a random sample of Americans. In a recent study by Pew Research Genter[40], Twitter users are discovered to be younger, wealthier, and more educated than the United States at large. They are also modestly more liberal and more likely to say that voted in the last election. So it is conceivable that Twitter users are also more likely to adopt political identities than the general population. More data would be necessary to resolve this ambiguity. But we think that a general politicization of social identity is consistent with the other measures of politicization that we referenced in Section 1—voter turnout, affective polarization, cultural sorting, and so on. Further, our sampling method samples tweets rather than users. Users who do not use tweets—who may have an account only to receive information or direct message—are thus not observed. These users may be systematically different from our sample of users who do use tweets, and the present method cannot speak to whether their self-identification is changing or not. A third issue is the construction of our lists of keywords. We were sensitive to the possibility that certain “trendy” keywords could increase in prevalence not because individuals are defining themselves more politically, but rather because the keywords themselves are becoming more popular and supplanting “outdated” keywords that are not in our lists. For example, a hypothetical Twitter user might have had an Obamasupportive “Yes We Can” phrase in their bio in 2015, but swapped it out in 2016 for a “Nasty Woman” reference to Hillary Clinton. Because the former phrase is not in our list, and the latter phrase is, our method would give the misleading impression that the user had “politicized” their bios, when in fact it was political all along. We considered a number of methods that might limit the amount of subjectivity of that process. We searched for an adequate pre-existing keyword set, to no compelling avail. We analyzed the Twitter bios of several dozens of popular political figures, to see what descriptors they commonly employed. To our surprise, these individuals rarely used words that were even implicitly partisan, in their bios . We contemplated various Natural Language Processing techniques, to obtain frequently-used words on political hotbeds such as Reddit’s r/politics subreddit. But ultimately we concluded that the utility of such methods would be outweighed by the drawbacks and complications. Future research may build upon these results by constructing more comprehensive (or selective) banks of keywords. It would also be fruitful to expand upon these descriptive data and incorporate more layered analyses. With demographic information on our Twitter users, for example, we could conduct models to determine which characteristics are most correlated with changes in political identity. We could also analyze the users’ tweets over time (rather than merely their bios), and analyze what sorts of rhetoric tends to portend or reflect a recent change of identity. Continued inquiry on the matter is important: It is crucial to understand the dynamics underlying American political polarization. The stability of a people is dependent on some sense of unifying solidarity. Without it, order is imperiled and chaos invited.