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

home_cover

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

home_cover

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

home_cover

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

home_cover

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.