Just Published! Social TV and Pluralism in Talk Shows

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From contents to comments:

Social TV and perceived pluralism in political talk shows

New Media & Society (Journal IF: 3.110)

Co-author: Sergio Splendore

Acknowledgments: Voices from the Blogs; Giovanni De Stasio

 

What is worth remembering:

  • We locate the audience of talk shows in a two-dimensional space based on positive and negative sentiment expressed toward guest politicians
  • We evalute pluralism and audience fragmentation accordingly
  • Public television offers a plural set of talk shows but ignores the antipolitical audience
  • Across media networks, there exists a variety of shows appealing to different audiences
  • We find a statistically significant difference between the average left-right position of the shows presented by left-wing or right-wing hosts
  • There is no gender bias: female guests are not evaluated more negatively than males

Abstract

Going beyond source and content pluralism, we propose a two-dimensional audiencebased measure of perceived pluralism by exploiting the practice of “social TV”. For this purpose, 135,228 tweets related to 30 episodes of prime time political talk shows broadcast in Italy in 2014 have been analyzed through supervised sentiment analysis. The findings suggest that the two main TV networks compete by addressing generalist audiences. The public television offers a plural set of talk shows but ignores the antipolitical audience. The ideological background of the anchorman shapes the audience’s perception, while the gender of the guests does not seem to matter.

Just Published! iSA: supervised aggregated sentiment analysis of social media

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iSA: a fast, scalable and accurate algorithm for sentiment analysis of social media content

Information Sciences (Journal IF: 4.038)

Co-authors: Luigi Curini & Stefano M. Iacus

Replication material: www.sciencedirect.com

What is worth remembering:

  • The new algorithm iSA for sentiment/opinion analysis is presented
  • iSA is fast, scalable, accurate and language independent
  • iSA is stable if the number of classes/opinions is large and allows for cross-tabulation
  • iSA works in the case of random and non-random sampling

Abstract

We present iSA (integrated Sentiment Analysis), a novel algorithm designed for social networks and Web 2.0 sphere (Twitter, blogs, etc.) opinion analysis, i.e. developed for the digital environments characterized by abundance of noise compared to the amount of information. Instead of performing an individual classification and then aggregate the predicted values, iSA directly estimates the aggregated distribution of opinions. Based on supervised hand-coding rather than NLP techniques or ontological dictionaries, iSA is a language-agnostic algorithm (based on human coders’ abilities). iSA exploits a dimensionality reduction approach which makes it scalable, fast, memory efficient, stable and statistically accurate. The cross-tabulation of opinions is possible with iSA thanks to its stability. Through empirical analysis it will be shown when iSA outperforms machine learning techniques of individual classification (e.g. SVM, Random Forests, etc) as well as the only other alternative for aggregated sentiment analysis known as ReadMe.

Just Published! Public Policy & Mobilization of Online Public Opinion

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The “Social Side” of Public Policy: Monitoring Online Public Opinion and Its Mobilization During the Policy Cycle

Policy & Internet

Co-author: Fedra Negri

Acknowledgments: Voices from the Blogs for providing data

 

What is worth remembering:

  • We found similarities between 1) Survey data, 2) online Sentiment, 3) online Government Consultation
  • Social media data can disclose citizens’ reaction to public policies
  • Social media data can capture stakeholders’ mobilization and de-mobilization processes

Abstract

This article addresses the potential role played by social media analysis in promoting interaction between politicians, bureaucrats, and citizens. We show that in a “Big Data” world, the comments posted online by social media users can profitably be used to extract meaningful information, which can support the action of policymakers along the policy cycle. We analyze Twitter data through the technique of Supervised Aggregated Sentiment Analysis. We develop two case studies related to the “jobs act” labor market reform and the “#labuonascuola” school reform, both formulated and implemented by the Italian Renzi cabinet in 2014–15. Our results demonstrate that social media data can help policymakers to rate the available policy alternatives according to citizens’ preferences during the formulation phase of a public policy; can help them to monitor citizens’ opinions during the implementation phase; and capture stakeholders’ mobilization and de-mobilization processes. We argue that, although social media analysis cannot replace other research methods, it provides a fast and cheap stream of information that can supplement traditional analyses, enhancing responsiveness and institutional learning.

Just Published! Twitter vs Media: First and Second level Agenda Setting in Italy

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First and Second Level Agenda-Setting in the Twitter-Sphere. An Application to the Italian Political Debate

Journal of Information Technology & Politics

Co-authors: Luigi Curini & Stefano M. Iacus

Acknowledgments: Voices from the Blogs for providing data

What is worth remembering:

  • We analyze agenda-setting focusing on two salient issues in the Italian political debate: austerity and the public funding of parties (related to Euro-skepticism and anti-politics)
  • We compared Twitter and the Online News
  • Using a Lead-Lag statistical technique we find that mass media still retain
  • First-Level Agenda-Setting: They influence the Twitter-attention toward an issue
  • Journalists can act as watch-dogs as their action can promote further (public) discussion also on anti-establishment issues
  • Using Supervised Sentiment Analysis we find that mass media do not exert Second-Level Agenda-Setting: They do not influence the Twitter-attitudes toward an issue
  • We found a citizen-elite divide between the opinions expressed on SNS and the slant spread by the media elite

Abstract

The rise of Social Network Sites re-opened the debate on the ability of traditional media to influence the public opinion and act as agenda-setter. To answer this question, the present paper investigates first-level and second-level agenda-setting effects in the online environment by focusing on two Italian heated political debates (the reform of public funding of parties and the debate over austerity). By employing innovative and efficient statistical methods like the lead-lag analysis and supervised sentiment analysis, we compare the attention devoted to each issue and the content spread by online news media and Twitter users. Our results show that online media keep their first-level agenda-setting power even though we find a marked difference between the slant of online news and the Twitter sentiment.

Just Published! Social media electoral forecast: State-of-the-art

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Using Social Media to Forecast Electoral Results: A Review of the State of the Art

Italian Journal of Applied Statistics – Statistica Applicata

Co-authors: Luigi Curini & Stefano M. Iacus

Replication material: andreaceron.com/publications

Acknowledgments: Voices from the Blogs for providing data

What is worth remembering:

  • Many scholars tried to predict elections using social media
  • Some methods are better than others
  • Supervised sentiment analysis seems the best choice
  • Predictions are more accurate in countries with Proportional Representation

Abstract

The paper discusses the advantages of using Supervised Aggregated Sentiment Analysis (SASA) of social media to forecast electoral results and presents an extension of the ReadMe method (Hopkins and King, 2010), which is particularly suitable to addressing a large number of categories (e.g. parties) providing lower standard errors. We analyze the voting intention of social media users in several elections held between 2011 and 2013 in France, Italy, and the United States. We then compare 80 electoral forecasts made using these or other techniques of data-mining and sentiment analysis. The comparison shows that the choice of the method is crucial. Electoral forecasts are also more accurate in countries with higher Internet penetration and given the presence of electoral systems based on proportional representation.

Just Published! (Positive and Negative) E-campaigning on Twitter in the 2013 Italian election

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

New Media & Society (Journal IF: 2.052)

Co-author: Giovanna d’Adda

Replication material: andreaceron.com/publications

Acknowledgments: Voices from the Blogs; Alessandra Cremonesi; University of Birmingham

What is worth remembering:

  • Analysis of Twitter useful to investigate electoral campaign effects
  • Voting intentions on Twitter react to real events of the campaign
  • Negative campaign effective against rival adjacent parties
  • Negative campaign more effective when the attacker is under attack (voters close ranks!)
  • Negative campaign = more votes for PD (+1.31%) rather than PDL (+0.22%)
  • Distributive promises effective only when properly targeted
  • Distributive promises = more votes for Berlusconi’s PDL (+0.12%) but less for PD (-0.42%)
  • More “Spread” = more votes for Grillo’s M5S (+0.37%) and less for PD (-0.52%)

Abstract

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 related to clientelistic and distributive appeals.