Discovering Political Polarization on Social Media: A Case Study

Belcastro, Loris; Cantini, Riccardo; Marozzo, Fabrizio; Talia, Domenico; Trunfio, Paolo

doi:10.1109/skg49510.2019.00038

Cited by 8 publications

(7 citation statements)

References 16 publications

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“…This manuscript significantly extends a previous work [7] in the following main aspects: i) it provides an in-depth definition of the steps of the methodology and a formal description through pseudo-code of the algorithms used for classifying the posts and predicting the users' polarization (see Sections III-B and III-C); ii) it presents and discusses a new case study on which the methodology has been applied and tested (see Section IV); iii) it includes the evaluation of the statistical significance of the collected data for the two case studies; and iv) it reports more extensive and detailed tests and comparisons with relevant techniques used in the literature (see Section IV).…”

Section: It Can Considered As An Alternative Technique To Traditionalsupporting

confidence: 89%

“…• About 94% of the social media users in USA are adults (at least 18 years old) and almost equally divided by gender (42.7% females and 57.3% males). 7 The following keywords have been used in our experiments: Table 5 shows the results obtained using IOM-NN in comparison with the real voting percentages, the main opinion polls, and the other related techniques. For each state in the table, we reported the results obtained by the two candidates, where ''C'' stands for Clinton and ''T'' for Trump.…”

Section: B 2016 Us Presidential Electionmentioning

confidence: 99%

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Learning Political Polarization on Social Media Using Neural Networks

et al. 2020

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Social media analysis is a fast growing research area aimed at extracting useful information from social media platforms. This paper presents a methodology, called IOM-NN (Iterative Opinion Mining using Neural Networks), for discovering the polarization of social media users during election campaigns characterized by the competition of political factions. The methodology uses an automatic incremental procedure based on feed-forward neural networks for analyzing the posts published by social media users. Starting from a limited set of classification rules, created from a small subset of hashtags that are notoriously in favor of specific factions, the methodology iteratively generates new classification rules. Such rules are then used to determine the polarization of people towards a faction. The methodology has been assessed on two case studies that analyze the polarization of a large number of Twitter users during the 2018 Italian general election and 2016 US presidential election. The achieved results are very close to the real ones and more accurate than the average of the opinion polls, revealing the high accuracy and effectiveness of the proposed approach. Moreover, our approach has been compared to the most relevant techniques used in the literature (sentiment analysis with NLP, adaptive sentiment analysis, emoji-and hashtag-based polarization) by achieving the best accuracy in estimating the polarization of social media users. INDEX TERMS Social media analysis, opinion mining, user polarization, neural networks, sentiment analysis, political events.

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Section: It Can Considered As An Alternative Technique To Traditionalsupporting

confidence: 89%

Section: B 2016 Us Presidential Electionmentioning

confidence: 99%

Learning Political Polarization on Social Media Using Neural Networks

et al. 2020

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“…It is interesting to observe how the focus on populism serves as an extension of many previous lines of research: from the new forms of political protest (Chiaramonte et al, 2018;Mosca & Quaranta, 2017) to the ability of populist movements to set the agenda (Alonso-Muñoz & Casero-Ripollés, 2018). Populism in its various forms dominates the publications in both 2018 and 2019, with surprisingly few papers focusing on fake news (Bradshaw & Howard, 2018;Ferrari, 2018) and just a small amount more on polarization (Belcastro et al, 2019;Marozzo & Bessi, 2018), topics that received substantial research in other national contexts. It is not that these issues were ignored or deemed irrelevant in the Italian context.…”

Section: Ten Years Of Questionsmentioning

confidence: 98%

(Nearly) Ten Years of Social Media and Political Elections in Italy: Questions, Platforms, and Methods

Rossi

Righetti

Marino

2021

Social Media + Society

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In this article, we reconstruct the academic discourse surrounding social media and elections in an Italian context. We follow Neumayer and Rossi’s conceptualization of academic discourse concerning political protest and digital technology as constructed out of three components: (a) the social phenomena under investigation, (b) technological development, and (c) methods and techniques. In the context of social media and elections, these three components may be identified as (a) the research questions that researchers seek to answer, (b) the social media platforms and data used for the analysis, and (c) the methods adopted to analyze the data. While these three dimensions are deeply intertwined, we argue that, when analyzed independently, it is possible to better see both the longitudinal evolution of each dimension and their interdependencies.

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“…To do that, we report in Fig. 6 trends and numbers (i) of Twitter users we tracked attending at the matches during the World Cup, and (ii) of attendees officially published by the FIFA website 4 . Specifically, Fig.…”

Section: Study Cases: Fifa World Cup 2014 and Expo 2015mentioning

confidence: 99%

“…As discussed in [4], the algorithm is divided in two parts. The fist part performs the preliminary iteration (iteration 0).…”

Section: Classification Of Postsmentioning

confidence: 99%

Cloud Computing for Enabling Big Data Analysis

Belcastro

Marozzo

Talia

et al. 2021

Communications in Computer and Information Science

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Every day billions of people access web sites, blogs, and social media. Often they use their mobile devices and produce huge amount of data that can be effectively exploited for extracting valuable information concerning human dynamics and behaviors. Such data, commonly referred as Big Data, contains rich information about user activities, interests, and behaviors, which makes it intrinsically suited to a very large set of applications. For getting valuable information and knowledge from such data in a reasonable time, novel scalable frameworks and data analysis techniques on Cloud systems have been developed. This paper aims at describing some recent Cloud-based frameworks and methodologies for Big Data processing that can be used for developing and executing several data analysis applications, including trajectory mining and sentiment analysis. The paper is organized in two main parts. The first part focuses on tools for developing and executing scalable data analysis applications on Clouds. The second part presents data analysis methodologies for extracting knowledge from large datasets.

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Discovering Political Polarization on Social Media: A Case Study

Cited by 8 publications

References 16 publications

Learning Political Polarization on Social Media Using Neural Networks

Learning Political Polarization on Social Media Using Neural Networks

(Nearly) Ten Years of Social Media and Political Elections in Italy: Questions, Platforms, and Methods

Cloud Computing for Enabling Big Data Analysis

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