What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020

Alexander Shevtsov; Maria Oikonomidou; Despoina Antonakaki; Polyvios Pratikakis; Sotiris Ioannidis

doi:10.1371/journal.pone.0270542

What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020

PLoS One. 2023 Jan 31;18(1):e0270542. doi: 10.1371/journal.pone.0270542. eCollection 2023.

Authors

Alexander Shevtsov^{1

2}, Maria Oikonomidou^{1

2}, Despoina Antonakaki¹, Polyvios Pratikakis^{1

2}, Sotiris Ioannidis^{1

3}

Affiliations

¹ Institute of Computer Science, Foundation for Research and Technology, Vassilika Vouton, Heraklion, Crete, Greece.
² Computer Science Department - University of Crete, Voutes Campus, Heraklion, Crete, Greece.
³ School of Electrical and Computer Engineering, Technical University of Crete, University Campus, Akrotiri, Chania, Greece.

Abstract

Most studies analyzing political traffic on Social Networks focus on a single platform, while campaigns and reactions to political events produce interactions across different social media. Ignoring such cross-platform traffic may lead to analytical errors, missing important interactions across social media that e.g. explain the cause of trending or viral discussions. This work links Twitter and YouTube social networks using cross-postings of video URLs on Twitter to discover the main tendencies and preferences of the electorate, distinguish users and communities' favouritism towards an ideology or candidate, study the sentiment towards candidates and political events, and measure political homophily. This study shows that Twitter communities correlate with YouTube comment communities: that is, Twitter users belonging to the same community in the Retweet graph tend to post YouTube video links with comments from YouTube users belonging to the same community in the YouTube Comment graph. Specifically, we identify Twitter and YouTube communities, we measure their similarity and differences and show the interactions and the correlation between the largest communities on YouTube and Twitter. To achieve that, we have gather a dataset of approximately 20M tweets and the comments of 29K YouTube videos; we present the volume, the sentiment, and the communities formed in YouTube and Twitter graphs, and publish a representative sample of the dataset, as allowed by the corresponding Twitter policy restrictions.

Copyright: © 2023 Shevtsov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Attitude
Humans
Social Media*
Social Networking

Grants and funding

This document is the results of the research project co-funded by the European Commission, project CONCORDIA, with grant number 830927 (EUROPEAN COMMISSION Directorate-General Communications Networks, Content and Technology) and by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE - INNOVATE (project ode:T1EDK-02857 and T1EDK-01800). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.