Next Article in Journal
Analyzing COVID-19 Medical Papers Using Artificial Intelligence: Insights for Researchers and Medical Professionals
Previous Article in Journal
Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches
Previous Article in Special Issue
6G Cognitive Information Theory: A Mailbox Perspective
Article

Analyzing Political Polarization on Social Media by Deleting Bot Spamming

Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, 87036 Rende, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Min Chen
Big Data Cogn. Comput. 2022, 6(1), 3; https://doi.org/10.3390/bdcc6010003
Received: 15 November 2021 / Revised: 15 December 2021 / Accepted: 25 December 2021 / Published: 4 January 2022
(This article belongs to the Special Issue Big Data and Cognitive Computing: 5th Anniversary Feature Papers)
Social media platforms are part of everyday life, allowing the interconnection of people around the world in large discussion groups relating to every topic, including important social or political issues. Therefore, social media have become a valuable source of information-rich data, commonly referred to as Social Big Data, effectively exploitable to study the behavior of people, their opinions, moods, interests and activities. However, these powerful communication platforms can be also used to manipulate conversation, polluting online content and altering the popularity of users, through spamming activities and misinformation spreading. Recent studies have shown the use on social media of automatic entities, defined as social bots, that appear as legitimate users by imitating human behavior aimed at influencing discussions of any kind, including political issues. In this paper we present a new methodology, namely TIMBRE (Time-aware opInion Mining via Bot REmoval), aimed at discovering the polarity of social media users during election campaigns characterized by the rivalry of political factions. This methodology is temporally aware and relies on a keyword-based classification of posts and users. Moreover, it recognizes and filters out data produced by social media bots, which aim to alter public opinion about political candidates, thus avoiding heavily biased information. The proposed methodology has been applied to a case study that analyzes the polarization of a large number of Twitter users during the 2016 US presidential election. The achieved results show the benefits brought by both removing bots and taking into account temporal aspects in the forecasting process, revealing the high accuracy and effectiveness of the proposed approach. Finally, we investigated how the presence of social bots may affect political discussion by studying the 2016 US presidential election. Specifically, we analyzed the main differences between human and artificial political support, estimating also the influence of social bots on legitimate users. View Full-Text
Keywords: social bots; political polarization; influence spread; social media analysis social bots; political polarization; influence spread; social media analysis
Show Figures

Figure 1

MDPI and ACS Style

Cantini, R.; Marozzo, F.; Talia, D.; Trunfio, P. Analyzing Political Polarization on Social Media by Deleting Bot Spamming. Big Data Cogn. Comput. 2022, 6, 3. https://doi.org/10.3390/bdcc6010003

AMA Style

Cantini R, Marozzo F, Talia D, Trunfio P. Analyzing Political Polarization on Social Media by Deleting Bot Spamming. Big Data and Cognitive Computing. 2022; 6(1):3. https://doi.org/10.3390/bdcc6010003

Chicago/Turabian Style

Cantini, Riccardo, Fabrizio Marozzo, Domenico Talia, and Paolo Trunfio. 2022. "Analyzing Political Polarization on Social Media by Deleting Bot Spamming" Big Data and Cognitive Computing 6, no. 1: 3. https://doi.org/10.3390/bdcc6010003

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop