Freedom to Tweet or Tweet to Freedom: The Relationship between Freedom Status and Tweets during Elections

In this thesis, I conduct an exploratory study of the relationship between a country's freedom and the twitter activity during elections. While there have been many studies of Twitter and elections, there has been no previous research conducted to explore the relationship between a countries' freedom and how Twitter influences elections in that given country. My goal is to identify hypotheses for future work in this area, introduce research designs and to shed light on areas of research where there seems to be little indication of relationships. I explore this space with automated analysis of the tweets' text, election outcomes, freedom ratings for the countries, and sentiment analysis. My results show that there seems to be a weak relationship between the outcome of an election and the sentiment expressed towards a candidate in tweets and that there is no relationship between the freedom in a given country and the sentiment expressed towards the incumbent. I found promising initial results regarding the relationship among content removed from links during an election and freedom status of a country, as well as the correlation between how frequently a candidate is mentioned and the election outcome. In the discussion, I present research questions in areas that are promising for future work

Polarization and acculturation in US Election 2016 outcomes – Can twitter analytics predict changes in voting preferences

Elections are among the most critical events in a national calendar. During elections, candidates increasingly use social media platforms to engage voters. Using the 2016 US presidential election as a case study, we looked at the use of Twitter by political campaigns and examined how the drivers of voter behaviour were reflected in Twitter. Social media analytics have been used to derive insights related to theoretical frameworks within political science. Using social media analytics, we investigated whether the nature of social media discussions have an impact on voting behaviour during an election, through acculturation of ideologies and polarization of voter preferences. Our findings indicate that discussions on Twitter could have polarized users significantly. Reasons behind such polarization were explored using Newman and Sheth's model of voter's choice behaviour. Geographical analysis of tweets, users, and campaigns suggests acculturation of ideologies among voting groups. Finally, network analysis among voters indicates that polarization may have occurred due to differences between the respective online campaigns. This study thus provides important and highly relevant insights into voter behaviour for the future management and governance of successful political campaigns.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Information and Communication Technolog

Like, share, vote

This report explores the potential for social media to support efforts to get out the vote. Overview Across Europe, low voter turnout in European and national elections is a growing concern. Many citizens are disengaged from the political process, threatening the health of our democracies. At the same time, the increasingly prominent role that social media plays in our lives and its function as a new digital public space offers new opportunities to reengage non-voters. This report explores the potential for social media to support efforts to get out the vote. It lays out which groups need to be the focus of voter mobilisation efforts, and makes the case for using social media campaigning as a core part of our voter mobilisation efforts. The research draws on a series of social media voter mobilisation workshops run by Demos with small third sector organisations in six target countries across Europe, as well as expert interviews, literature review and social media analysis. Having affirmed the need for and utility of social media voter turnout efforts, Like, Share, Vote establishes key principles and techniques for a successful social media campaign: how to listen to the digital discourse of your audience, how to use quizzes and interactive approaches, how to micro-target specific groups and how to coordinate offline events with online campaigns. This report concludes that, with more of our social and political lives taking place online than ever before, failing to use social media to reinvigorate our democracy would be a real missed opportunity

Una revisión del análisis político mediante la web social

En los países democráticos, conocer la intención de voto de los ciudadanos y las valoraciones de los principales partidos y líderes políticos es de gran interés tanto para los propios partidos como para los medios de comunicación y el público en general. Para ello se han utilizado tradicionalmente costosas encuestas personales. El auge de las redes sociales, principalmente Twitter, permite pensar en ellas como una alternativa barata a las encuestas. En este trabajo, revisamos la bibliografía científica más relevante en este ámbito, poniendo especial énfasis en el caso español.In democratic countries, forecasting the voting intentions of citizens and knowing their opinions on major political parties and leaders is of great interest to the parties themselves, to the media, and to the general public. Traditionally, expensive polls based on personal interviews have been used for this purpose. The rise of social networks, particularly Twitter, allows us to consider them as a cheap alternative. In this paper, we review the relevant scientific bibliographic references in this area, with special emphasis on the Spanish case.This research is partially supported by Ministerio de Economía y Competitividad (FFI2014-51978-C2). David Vilares is partially funded by the Ministerio de Educación, Cultura y Deporte (FPU13/01180)

Una revisión del análisis político mediante la web social

[Abstract] In democratic countries, forecasting the voting intentions of citizens and knowing their opinions on major political parties and leaders is of great interest to the parties themselves, to the media, and to the general public. Traditionally, expensive polls based on personal interviews have been used for this purpose. The rise of social networks, particularly Twitter, allows us to consider them as a cheap alternative. In this paper, we review the relevant scientific bibliographic references in this area, with special emphasis on the Spanish case.[Resumen] En los países democráticos, conocer la intención de voto de los ciudadanos y las valoraciones de los principales partidos y líderes políticos es de gran interés tanto para los propios partidos como para los medios de comunicación y el público en general. Para ello se han utilizado tradicionalmente costosas encuestas personales. El auge de las redes sociales, principalmente Twitter, permite pensar en ellas como una alternativa barata a las encuestas. En este trabajo, revisamos la bibliografía científica más relevante en este ámbito, poniendo especial énfasis en el caso español.Ministerio de Economía y Competitividad; FFI2014-51978-C2Ministerio de Educación, Cultura y Deporte; FPU13/0118

Socio-Linguistic Characteristics of Coordinated Inauthentic Accounts

Online manipulation is a pressing concern for democracies, but the actions and strategies of coordinated inauthentic accounts, which have been used to interfere in elections, are not well understood. We analyze a five million-tweet multilingual dataset related to the 2017 French presidential election, when a major information campaign led by Russia called "#MacronLeaks" took place. We utilize heuristics to identify coordinated inauthentic accounts and detect attitudes, concerns and emotions within their tweets, collectively known as socio-linguistic characteristics. We find that coordinated accounts retweet other coordinated accounts far more than expected by chance, while being exceptionally active just before the second round of voting. Concurrently, socio-linguistic characteristics reveal that coordinated accounts share tweets promoting a candidate at three times the rate of non-coordinated accounts. Coordinated account tactics also varied in time to reflect news events and rounds of voting. Our analysis highlights the utility of socio-linguistic characteristics to inform researchers about tactics of coordinated accounts and how these may feed into online social manipulation.Comment: 12 pages, 9 figure

A Large-Scale Sentiment Analysis of Tweets Pertaining to the 2020 US Presidential Election

We capture the public sentiment towards candidates in the 2020 US Presidential Elections, by analyzing 7.6 million tweets sent out between October 31st and November 9th, 2020. We apply a novel approach to first identify tweets and user accounts in our database that were later deleted or suspended from Twitter. This approach allows us to observe the sentiment held for each presidential candidate across various groups of users and tweets: accessible tweets and accounts, deleted tweets and accounts, and suspended or inaccessible tweets and accounts. We compare the sentiment scores calculated for these groups and provide key insights into the differences. Most notably, we show that deleted tweets, posted after the Election Day, were more favorable to Joe Biden, and the ones posted leading to the Election Day, were more positive about Donald Trump. Also, the older a Twitter account was, the more positive tweets it would post about Joe Biden. The aim of this study is to highlight the importance of conducting sentiment analysis on all posts captured in real time, including those that are now inaccessible, in determining the true sentiments of the opinions around the time of an event

The Pulse of Mood Online: Unveiling Emotional Reactions in a Dynamic Social Media Landscape

The rich and dynamic information environment of social media provides researchers, policy makers, and entrepreneurs with opportunities to learn about social phenomena in a timely manner. However, using these data to understand social behavior is difficult due to heterogeneity of topics and events discussed in the highly dynamic online information environment. To address these challenges, we present a method for systematically detecting and measuring emotional reactions to offline events using change point detection on the time series of collective affect, and further explaining these reactions using a transformer-based topic model. We demonstrate the utility of the method by successfully detecting major and smaller events on three different datasets, including (1) a Los Angeles Tweet dataset between Jan. and Aug. 2020, in which we revealed the complex psychological impact of the BlackLivesMatter movement and the COVID-19 pandemic, (2) a dataset related to abortion rights discussions in USA, in which we uncovered the strong emotional reactions to the overturn of Roe v. Wade and state abortion bans, and (3) a dataset about the 2022 French presidential election, in which we discovered the emotional and moral shift from positive before voting to fear and criticism after voting. The capability of our method allows for better sensing and monitoring of population's reactions during crises using online data.Comment: arXiv admin note: substantial text overlap with arXiv:2307.1024