Learning Political Polarization on Social Media Using Neural Networks

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

doi:10.1109/access.2020.2978950

Cited by 68 publications

(45 citation statements)

References 19 publications

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“…In another example of the use of word embeddings [38], a neural network was trained to learn word cooccurrences and generate word vectors from a corpus of four million political tweets extracted during the EU referendum of 23 June 2016. With a similar scope, [39] attempted to successfully estimate the polarization of social media users in the 2018 Italian general election and the 2016 US presidential election using a new methodology called Iterative Opinion Mining using Neural Networks (IOM-NN). Additionally, [40] proposed a cloud-based algorithm to perform similar analyses.…”

Section: A Related Workmentioning

confidence: 99%

Sentiment Analysis of Political Tweets From the 2019 Spanish Elections

et al. 2021

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The use of sentiment analysis methods has increased in recent years across a wide range of disciplines. Despite the potential impact of the development of opinions during political elections, few studies have focused on the analysis of sentiment dynamics and their characterization from statistical and mathematical perspectives. In this paper, we apply a set of basic methods to analyze the statistical and temporal dynamics of sentiment analysis on political campaigns and assess their scope and limitations. To this end, we gathered thousands of Twitter messages mentioning political parties and their leaders posted several weeks before and after the 2019 Spanish presidential election. We then followed a twofold analysis strategy: (1) statistical characterization using indices derived from well-known temporal and information metrics and methods -including entropy, mutual information, and the Compounded Aggregated Positivity Index-allowing the estimation of changes in the density function of sentiment data; and (2) feature extraction from nonlinear intrinsic patterns in terms of manifold learning using autoencoders and stochastic embeddings. The results show that both the indices and the manifold features provide an informative characterization of the sentiment dynamics throughout the election period. We found measurable variations in sentiment behavior and polarity across the political parties and their leaders and observed different dynamics depending on the parties' positions on the political spectrum, their presence at the regional or national levels, and their nationalist or globalist aspirations.

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Section: A Related Workmentioning

confidence: 99%

Sentiment Analysis of Political Tweets From the 2019 Spanish Elections

et al. 2021

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“…IOM-NN (Iterative Opinion Mining using Neural Networks) [5] is a new methodology for estimating the polarization of public opinion on political events characterized by the competition of factions or parties. It can considered as an alternative technique to traditional opinion polls, since it is able to capture the opinion of a larger number of people more quickly and at a lower cost.…”

Section: Iterative Opinion Mining Using Neural Networkmentioning

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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“…This paper presents some of the most recent activities we carried out in the field of Social Big Data analysis. In particular, it discusses how these research activities can be exploited and, if necessary, combined for the analysis of large amounts of social media data, aimed at extracting different kind of knowledge from three different perspectives: (i) from the posts published by tourists who visit a city, we can discover the main tourist attractions and also the mobility patterns (i.e., trajectories) across them [7]; (ii) from public discussions on social media close to important electoral events, it is possible to discover the political orientation of citizens and thus estimate the outcome of a political event [8]; (iii) from hashtags used by social media users, we discover the main topics underlying social media conversation and how users refer to them in publishing online content [9].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Discovery from Large Amounts of Social Media Data

2022

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In recent years, social media analysis is arousing great interest in various scientific fields, such as sociology, political science, linguistics, and computer science. Large amounts of data gathered from social media are widely analyzed for extracting useful information concerning people’s behaviors and interactions. In particular, they can be exploited to analyze the collective sentiment of people, understand the behavior of user groups during global events, monitor public opinion close to important events, identify the main topics in a public discussion, or detect the most frequent routes followed by social media users. As an example of the countless works in the state-of-the-art on social media analysis, this paper presents three significant applications in the field of opinion and pattern mining from social media data: (i) an automatic application for discovering user mobility patterns, (ii) a novel application for estimating the political polarization of public opinion, and (iii) an application for discovering interesting social media discussion topics through a hashtag recommendation system. Such applications clearly highlight the abundance and wealth of useful information in many application contexts of human life that can be extracted from social media posts.

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Learning Political Polarization on Social Media Using Neural Networks

Cited by 68 publications

References 19 publications

Sentiment Analysis of Political Tweets From the 2019 Spanish Elections

Sentiment Analysis of Political Tweets From the 2019 Spanish Elections

Cloud Computing for Enabling Big Data Analysis

Knowledge Discovery from Large Amounts of Social Media Data

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