Modelling political disaffection from Twitter data

Monti, Corrado; Rozza, Alessandro; Zappella, Giovanni; Zignani, Matteo; Arvidsson, Adam; Colleoni, Elanor

doi:10.1145/2502069.2502072

Cited by 28 publications

(14 citation statements)

References 18 publications

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“…As shown in Table , the quality of the feature extraction and classification model is confirmed by the experimental results obtained through 10‐fold cross‐validation on the PolTrainingSet . These results are consistent with those achieved in related works (Monti et al, ). The 10‐fold cross‐validation consists of randomly partitioning the original sample into 10 equal size subsamples.…”

Section: Predicting Political Orientationsupporting

confidence: 94%

Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data

2014

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This paper investigates political homophily on Twitter. Using a combination of machine learning and social network analysis we classify users as Democrats or as Republicans based on the political content shared. We then investigate political homophily both in the network of reciprocated and nonreciprocated ties. We find that structures of political homophily differ strongly between Democrats and Republicans. In general, Democrats exhibit higher levels of political homophily. But Republicans who follow official Republican accounts exhibit higher levels of homophily than Democrats. In addition, levels of homophily are higher in the network of reciprocated followers than in the nonreciprocated network. We suggest that research on political homophily on the Internet should take the political culture and practices of users seriously.

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Section: Predicting Political Orientationsupporting

confidence: 94%

Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data

2014

Self Cite

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“…Twitter can also be used to identify political preferences [54] and for day-by-day monitoring of electoral campaigns [55]- [57]. It is also possible to estimate the level of disaffection across people by counting negative tweets about politics in general, and this approach has shown that peaks in disaffection can correlate with important political news [58]. Other research studies explored how Twitter is applied within the electoral context to forecast electoral results [55], [59] by discovering candidates' patterns of political practice [60]- [62].…”

Section: A Related Workmentioning

confidence: 99%

Analysing and Visualizing Tweets for U.S. President Popularity

Luca

Fallucchi

Giuliano

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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In our society we are continually invested by a stream of information (opinions, preferences, comments, etc.). This shows how Twitter users react to news or events that they attend or take part in real time and with interest. In this context it becomes essential to have the appropriate tools in order to be able to analyze and extract data and information hidden in their large number of tweets. Social networks are a source of information with no rivals in terms of amount and variety of information that can be extracted from them. We propose an approach to analyze, with the help of automated tools, comments and opinions taken from social media in a real time environment. We developed a software system in R based on the Bayesian approach for text categorization. We aim of identifying sentiments expressed by the tweets posted on the Twitter social platform. The analysis of sentiment spread on social networks allows to identify the free thoughts, expressed authentically. In particular, we analyze the sentiments related to U.S President popularity by also visualizing tweets on a map. This allows to make an additional analysis of the real time reactions of people by associating the reaction of the single person who posted the tweet to his real time position in Unites States. In particular, we provide a visualization based on the geographical analysis of the sentiments of the users who posted the tweets.

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“…Monti et al [24] modeled the political disaffection on twitter, they randomly selected 50,000 Italian Twitter users, and collected their followers. The dataset analyzed contained 261,313 users and more than 35 million tweets from those users.…”

Section: Related Workmentioning

confidence: 99%

Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential election

Caetano

Lima

Santos

et al. 2018

J Internet Serv Appl

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This paper proposes an analysis of political homophily among Twitter users during the 2016 American Presidential Election. We collected 4.9 million tweets of 18,450 users and their contact network from August 2016 to November 2016. We defined six user classes regarding their sentiment towards Donald Trump and Hillary Clinton: whatever, Trump supporter, Hillary supporter, positive, neutral, and negative. Next, we analyzed their political homophily in three scenarios. Firstly, we analyzed the Twitter follow, mention and retweet connections either unidirectional and reciprocal. In the second scenario, we analyzed multiplex connections, and in the third one, we analyzed friendships with similar speeches. Our results showed that negative users, users supporting Trump, and users supporting Hillary had homophily in all analyzed scenarios. We also found out that the homophily level increase when there are reciprocal connections, similar speeches, or multiplex connections.

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Modelling political disaffection from Twitter data

Cited by 28 publications

References 18 publications

Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data

Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data

Analysing and Visualizing Tweets for U.S. President Popularity

Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential election

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