Social media monitoring in politics can be understood by situating it in theories of public opinion. The multimethod study we present here indicates how social media monitoring allow for analysis of social dynamics through which opinions form and shift. Analysis of media coverage from the 2010 UK General Election demonstrates that social media are now being equated with public opinion by political journalists. We use interviews with pollsters, social media researchers and journalists to examine the perceived link between social media and public opinion. In light of competing understandings these interviews reveal, we argue for a broadening of the definition of public opinion to include its social dimension.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jcc4.12102

Social Media Analysis and Public Opinion: The 2010 UK General Election

Demonstrations that analyses of social media content can align with measurement from sample surveys have raised the question of whether survey research can be supplemented or even replaced with less costly and burdensome data mining of already-existing or "found" social media content. But just how trustworthy such measurement can be-say, to replace official statistics-is unknown. Survey researchers and data scientists approach key questions from starting assumptions and analytic traditions that differ on, for example, the need for representative samples drawn from frames that fully cover the population. New conversations between these scholarly communities are needed to understand the potential points of alignment and non-alignment. Across these approaches, there are major differences in (a) how participants (survey respondents and social media posters) understand the activity they are engaged in; (b) the nature of the data produced by survey responses and social media posts, and the inferences that are legitimate given the data; and (c) practical and ethical considerations surrounding the use of the data. Estimates are likely to align to differing degrees depending on the research topic and the populations under consideration, the particular features of the surveys and social media sites involved, and the analytic techniques for extracting opinions and experiences from social media. Traditional population coverage may not be required for social media content to effectively predict social phenomena to the extent that social media content distills or summarizes broader conversations that are also measured by surveys.

Social Media Analyses for Social Measurement

Recent studies investigated the effect of e-campaigning on the electoral performance. However, little attention has been paid to the content of e-campaigning. Given that political parties broadcast minute-by-minute the campaign messages on social media, this comprehensive and unmediated information can be useful to evaluate the impact of different electoral strategies. Accordingly, this article examines the electoral campaign for the 2013 Italian general election to assess the effectiveness of positive and negative campaigning messages, measured through content analysis of information published on the official Twitter accounts of Italian parties. We evaluate their impact on the share of unsolicited voting intentions expressed on Twitter, measured through an innovative technique of sentiment analysis. Our results show that negative campaign has positive effects and its impact is stronger when the attacker is meanwhile under attack. Conversely, we only find a circumstantial effect of positive campaign relat...

E-campaigning on Twitter: The effectiveness of distributive promises and negative campaign in the 2013 Italian election

In this article, we examine the relationship between metrics documenting politics-related Twitter activity with election results and trends in opinion polls. Various studies have proposed the possibility of inferring public opinion based on digital trace data collected on Twitter and even the possibility to predict election results based on aggregates of mentions of political actors. Yet, a systematic attempt at a validation of Twitter as an indicator for political support is lacking. In this article, building on social science methodology, we test the validity of the relationship between various Twitter-based metrics of public attention toward politics with election results and opinion polls. All indicators tested in this article suggest caution in the attempt to infer public opinion or predict election results based on Twitter messages. In all tested metrics, indicators based on Twitter mentions of political parties differed strongly from parties’ results in elections or opinion polls. This leads us to question the power of Twitter to infer levels of political support of political actors. Instead, Twitter appears to promise insights into temporal dynamics of public attention toward politics.

/pdf/digital-trace-data-in-the-study-of-public-opinion-an-506jn7hem6.pdf

Digital Trace Data in the Study of Public Opinion: An Indicator of Attention Toward Politics Rather Than Political Support

Elections are the main instrument of democracy. Citizens decide which entity or entities (a political party or a particular politician) should represent them. Traditionally, pre-election polls have been used to learn about trends and likely election outcomes. Predicting an election outcome based on user activity on Twitter has been shown to be a cheap alternative. While past research has focused on election prediction in the developed world (where its use is debatable), in this paper we provide a comprehensive argument for the use of Twitter-based election forecasting in the developing world. For our use case of Indonesia's presidential elections 2014, the most basic Twitter-predictor outperforms the majority of traditional polls, while the best performing predictor outperforms all traditional polls on the national level.

Twitter-based Election Prediction in the Developing World

This paper analyzes the utility of using information contained within Twitter posts in predicting electoral outcomes. Particularly, we are interested in patterns in Twitter communications that can help explain differences between published opinion polls and the actual vote. We consider three categories of models. The first is a mentions model that examines the correspondence between the prevalence of communications about a candidate and electoral outcomes. The second series of models treat Twitter similar to a prediction market, aggregating not candidate preferences but predictions of the electoral result. Last, we consider whether the rediffusion of tweets about a candidate is a reliable predictor of the candidate's performance. The results find inconsistent support for the predictive value of Twitter mentions as an estimate of the overall vote, but these communications provide some evidence of otherwise undetected shifts in momentum with respect to the aggregated predictions of candidate performance and message rediffusion via retweets containing information about a particular candidate. Given the nature of the information extractable, these data are most sensitive to detecting changes in momentum.

Psephological Investigations: Tweets, Votes, and Unknown Unknowns in the Republican Nomination Process’, Policy & Internet, , .

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Psephological Investigations: Tweets, Votes, and Unknown Unknowns in the Republican Nomination Process’, Policy & Internet, , .