Just Published! March Divided, Fight United? Trade Unions and Government appeal for concertation

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March divided, fight united? Trade union cohesion and government appeal for concertation 

West European Politics (Journal IF: 2.512)

Co-author: Fedra Negri

Replication material: andreaceron.com/publications

 

What is worth remembering:

  • We measure policy preferences of trade unions by hand-coding unions’ congress motions 
  • When the government policy position is closer to trade unions, the government is more willing to appeal for concertation
  • When trade unions are more polarized, they become more appealing to the government as it can exploits unions’ division to negotiate a better deal

Abstract

Why does the government appeal for concertation? Starting from the principal‒agent framework and delegation theory, the article argues that the government is more willing to share decision-making power with trade unions when the policy preferences endorsed by the unions are closer to those of the cabinet. Furthermore, it maintains that government propensity to negotiate with trade unions increases as the heterogeneity of union policy preferences grows because the cabinet can exploit its agenda-setting power to divide the union front. The article tests these two hypotheses through a longitudinal analysis of the Italian case (1946–2014). In detail, it takes advantage of two original datasets built through content analysis that provide unique in-depth information on the policy preferences of parties and cabinets and measures the policy positions of the main Italian trade unions, thus allowing assessment of their reciprocal heterogeneity. The results confirm the expectations.

Just Published! Rotten apples spoil the ballot: Effect of corruption on parties’ vote shares

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When rotten apples spoil the ballot: The conditional effect of corruption charges on parties’ vote shares

International Political Science Review (Journal IF: 0.954)

Co-author: Marco Mainenti

Replication material: andreaceron.com/publications

 

What is worth remembering:

  • The electoral system moderates the effect of corruption charges on parties’ vote shares
  • Internal party rules related to selection of candidates moderate the effect of corruption charges on parties’ vote shares
  • To avoid the negative consequences of corruption charges on electoral performances, legislators could adopt an open list system or decentralized intra-party rules, preserving voters’ loyalty by allowing them to select individual candidates.
  • This has implications for the debate on the Italian electoral reform, given that some peculiar rules can limit the rise of anti-system parties when corruption scandals occur.

Abstract

The impact of corruption charges on the electoral performance of parties is conditioned by specific institutional factors. This article shows the extent to which the effects of political corruption depend on the control that party leaders exercise over the ballot. It is argued that voters might abstain or support other lists if they cannot select individual candidates to revitalize the reputation of the political party. Employing data on judicial investigations in Italy from 1983 to 2013, we provide evidence of the role of electoral rules and intra-party candidate selection in shaping the relationship between corruption and voters’ behaviour. Parties implicated in corruption or related crimes experience a loss of votes when they compete under a closed list formula or when the candidate selection process is strongly centralized.

Portfolio: Assessment of my 3-year assistant professorship

This summer I was asked (by my University) to fill the attached self-evaluation portfolio, which records all my achievements during this 3-year term. Since I had to spend quite a lot of time to fill this, and since I believe in transparency and in the evaluation of civil servants, I’m glad to make it publicly available. I’m pretty sure this is not a waste of time (I mean, filling a portfolio is more than filling a CV that you can find everywhere, isn’it? or not?). Anyway, taxpayers have the right to know how their money have been spent and what is the product of my work.

So, here it is: DOWNLOAD MY PORTFOLIO

And for the lazy ones, below you can find a summary #pleasejudgeme #misonopresosulseriale

SUMMARY

My key research achievements in this 31 months, in 2 different macro-areas of social sciences (parties/political institutions & social media/communication):
3 books published with Palgrave, Routledge and Springer (2 in English, 1 single-authored)
24 papers published in peer-reviewed journals during these 31 months (29 from the beginning of my career); 22 are in journals indexed in Scopus.
17 out of 24 in journals with Impact Factor. Average journal Impact Factor: 2.028; Impact Factor higher than 3 for 6 papers.
– Number of citations of these 24 papers: 234 (Google Scholar); 75 (Scopus). Average citations per each paper already registered in Scopus: 5.3
H-Index: 8 (Google Scholar); 4 (Scopus)
I have been co-founder and I am currently board member of Voices from the Blogs Srl, a spin-off of the University of Milan. It has filed a patent request (that is pending in the United States) concerning a new algorithm for supervised aggregated sentiment analysis (iSA). A free R package is made available too.

Through this spin-off I have been involved in several projects involving big enterprises, political parties and other public institutions. As a member of Voices from the Blogs I am currently serving as political analyst for the Italian Government (weekly monitoring online sentiment on a variety of topics).

I also wrote almost 200 articles in newspapers, magazines and academic blogs (mostly on analysis of social media but I am used to disseminating my research findings on every topic, including the study of political institutions)

I have started 2 big international collaborations. I am the co-founder of the “Party Congress Research Group”, project that aims at collecting and analyzing speeches delivered at intra-party conferences; I am also involved in the project “The Politics of Portfolio Design”, which aims at studying changes in ministerial organization across countries and over time.

As a member of Voices from the Blogs I have been involved in preparing several H2020 grant proposals one of which has got a score of 13.5/15 and has been included in the reserve list. I have also presented an individual proposal for the ERC Starting Grant.

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! Intra-party politics in 140 characters

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Intra-party politics in 140 characters

Party Politics (Journal IF: 1.271)

Acknowledgements: Alessandra Cremonesi for help
with data collection; participants and organizers of the La
Pietra Dialogues on Social Media and Political Participation
(Firenze, May 10–11, 2013) and Workshop on intraparty
politics in Europe (Gotheborg, September 17–18, 2015).

Replication material: andreaceron.com/publications

 What is worth remembering:

  • Politicians’ language online seems ideological in nature (at least in Italy)
  • When this is the case, by analyzing social media posts through automated text analysis we can assess policy position of intra-party subgroups and individual politicians
  • Their comments are informative on dissent, legislative behavior and career perspectives

Abstract

Scholars have emphasized the need to deepen investigation of intraparty politics. Recent studies look at social media as a source of information on the ideological preferences of politicians and political actors. In this regard, the present article tests whether social media messages published by politicians are a suitable source of data. It applies quantitative text analysis to the public statements released by politicians on social media in order to measure intraparty heterogeneity and assess its effects. Three different applications to the Italian case are discussed. Indeed, the content of messages posted online is informative on the ideological preferences of politicians and proved to be useful to understand intraparty dynamics. Intraparty divergences measured through social media analysis explain: (a) a politician’s choice to endorse one or another party leader, (b) a politician’s likelihood to switch off from his or her parliamentary party group; and (c) a politician’s probability to be appointed as a minister.

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.

Call for Papers! Big Data, Digital Data, Textual Data. Milan 15-17 Sept 2016

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Big Data, Digital Data, Textual Data: Restructuring Political Science? 

Chairs: Andrea Ceron & Luigi Curini (Università degli Studi di Milano)

Where and When: Milan, 15-17 September 2016.

Deadline to submit abstract: 5 June 2016

Link to: panel description. Submit here!

Call for Papers:

This panel is open to scholars from very different fields, ranging from political science to communication or computer science and information technology. The aim is to gather papers that adopt updated statistical methods (including text analyses techniques) to analyze large-N collections of digital data, either in textual or non-textual form. Any application of Big Data analysis (i.e. open data, social media data, or any large digital textual data) to the study of political institutions or to the study of public opinion dynamics is particularly welcome, but the panel also accepts papers related to other different topics linked with politics and society. Secondary analyses of Big Data performed through traditional statistical techniques are suitable too, particularly if these studies deal with the integration of different sources of data (e.g., survey data and sentiment analysis) or combine datasets from multiple sources (e.g. roll call votes, manifesto data, data on conflicts, pieces of news, etc.). We accept both case studies or longitudinal analyses related to Italy or to any other country, as well as cross-sectional comparative analyses that focus on more countries (related to the present or to the past). Thanks to such contributions, the panel aims to show how the “Big Data revolution” can allow us to solve puzzles involving traditional political science topics (e.g. legislative politics, coalition governments, electoral campaigns, accountability and responsiveness, peace and conflicts, democratization, collective action, agenda setting, etc.).

Context:

Political science is undergoing a complex threefold process of revolution, which can be summarized under the label of “Big Data revolution”. Political science is radically changing, from using sparse datasets produced by isolated scholars that work alone, to building up collaborative, interdisciplinary, lab-style research teams that analyze increasing quantities of diverse, highly informative data. Such transformation, from studying problems to solving them, can explain why – at least in some countries – “the influence of quantitative social science (including the related technologies, methodologies, and data) on the real world has been growing fast” (King 2014).

Big Data (i.e. large-N digital or textual data) certainly play a crucial role in such transformation and can contribute to restructuring political science. This process, in fact, benefits from different sources of data that are more and more available to scholars: 1) open data, provided by public or private organizations; 2) a wide array of textual data, produced by political institutions, which are increasingly available in a digital format; 3) digital data, in textual and non-textual form, generated by a growing crowd composed of Internet users and social media users (encompassing citizen-to-citizen and citizen-to-elite interactions, online news, and top-down elite communication).

Such “Big Data revolution” is not only related to data sources. The evolution of our societies toward a “digital world” is a necessary premise. However, the methodological contribution of information technology, which allows us to gather and store huge quantities of data, processing them at an incredibly fast rate, and the new developments in statistics and political methodology, particularly in the field of text analysis (Grimmer and Stewart 2013), are also important in performing such transformation.

Indeed, the recent improvements in terms of automated and supervised text analysis techniques dramatically reduce the costs of analyzing large collections of textual data and allow scholars to study politics and political conflicts through the analysis of written and spoken words. In this regard, a wide range of techniques is now increasingly used by political scientists. These methods range from scaling techniques – like Wordscore (Laver, Benoit and Garry 2003) and Wordfish (Slapin and Proksch 2008) – that measure similarities and differences between political actors, to topic models (Grimmer 2010; Quinn 2010) – that allows scholars to identify the topics discussed in a text.

These techniques can greatly enhance our knowledge on the functioning of political institutions, particularly when applied to large digital data gathered by collective research groups such as the Comparative Agenda Project (Baumgartner, Green-Pedersen and Jones 2006) or the Comparative Manifesto (Lehmann et al. 2015).

The broadening of Internet penetration and the increasing number (30% of world population in 2015) of worldwide citizens active on social networking sites, like Facebook and Twitter, pushed such revolution further. In this new “digital world” citizens share information and opinions online, thereby generating a large amount of data about their tastes and attitudes. The evolution of sentiment analysis (Hopkins and King 2010; Ceron, Curini and Iacus 2016) allows to extract information from these rich sources.

This information can then be successfully exploited to study more in depth the formation and evolution of public opinion (Schober et al. 2016) – particularly by integrating sentiment analysis with traditional survey data (Couper 2013) – in order to study political mobilization (Bennett and Segerberg 2011) or to nowcast and forecast elections (Ceron et al. 2014; Gayo-Avello 2013).

References

Baumgartner, Frank R., Christoffer Green-Pedersen, and Bryan D. Jones, eds. 2006. Comparative Studies of Policy Agendas. Special issue of the Journal of European Public Policy 13 (7).

Bennett, W.L. and Segerberg, A. (2011). Digital media and the personalization of collective action: Social technology and the organization of protests against the global economic crisis. In Information Communication and Society, 14(6): 770–799.

Ceron, Andrea, Luigi Curini and Stefano M. Iacus. 2016. Social Media and Politics: Nowcasting and Forecasting Elections with Big Data, London: Ashgate, forthcoming, 2016

Ceron, Andrea, Luigi Curini, Stefano M. Iacus, and Giuseppe Porro. 2014. “Every Tweet Counts? How Sentiment Analysis of Social Media Can Improve Our Knowledge of Citizens’ Political Preferences with an Application to Italy and France.” New Media & Society 16:340–58.

Couper, Mick P. 2013. “Is the Sky Falling? New Technology, Changing Media, and the Future of Surveys.” Survey Research Methods 7(3):145–56.

Gayo-Avello, D. (2013). A meta-analysis of state-of-the-art electoral prediction from Twitter data. In Social Science Computer Review, 31(6): 649–679.

Grimmer, J. and Stewart, B.M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. In Political Analysis, 21(3): 267–297.

Hopkins, Daniel, and Gary King. 2010. Extracting systematic social science meaning from text. American Journal of Political Science 54(1):229–47.

King, G. (2014). Restructuring the social sciences: Reflections from Harvard’s Institute for Quantitative Social Science. In Politics and Political Science, 47(1): 165–172.

Laver, Michael, Kenneth Benoit, and John Garry. 2003. Extracting policy positions from political texts using words as data. American Political Science Review 97(02):311–31.

Lehmann P, Matthieß T, Merz N, Regel S, Werner, A (2015) Manifesto Corpus. Version: 2015a. Berlin: WZB Berlin Social Science Center.

Quinn, Kevin. 2010. How to analyze political attention with minimal assumptions and costs. American Journal of Political Science 54(1):209–28.

Schober, Michael F., Pasek, Josh, Guggenheim, Lauren, Lampe, Cliff, and Conrad, Frederick G. (2016). Social media analyses for social measurement. Public Opinion Quarterly 80(1) 180–211

Slapin, Jonathan, and Sven-Oliver Proksch. 2008. A scaling model for estimating time-series party positions from texts. American Journal of Political Science 52(3):705–22.