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

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          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.

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          Most cited references70

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            Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: Funding acquisitionRole: Project administrationRole: ResourcesRole: Supervision
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2023
                31 January 2023
                : 18
                : 1
                : e0270542
                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
                La Trobe University - Melbourne Campus: La Trobe University, AUSTRALIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-5072-5569
                https://orcid.org/0000-0002-0251-3825
                https://orcid.org/0000-0001-9081-6115
                Article
                PONE-D-20-37937
                10.1371/journal.pone.0270542
                9888715
                36719868
                922efe94-13ff-4207-9548-d05a73d8ec14
                © 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.

                History
                : 11 December 2020
                : 30 May 2022
                Page count
                Figures: 19, Tables: 4, Pages: 31
                Funding
                Funded by: European Commission, project CONCORDIA
                Award ID: 830927
                Funded by: European Union and Greek National Funds
                Award ID: T1EDK-02857, and T1EDK-01800
                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.
                Categories
                Research Article
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Twitter
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Political Science
                Elections
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Sciences
                Sociology
                Social Networks
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Biology and Life Sciences
                Psychology
                Emotions
                Social Sciences
                Psychology
                Emotions
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                Computer and Information Sciences
                Software Engineering
                Preprocessing
                Engineering and Technology
                Software Engineering
                Preprocessing
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Custom metadata
                In order to obtain the dataset used for the analysis described in this study, we follow the Twitter API restrictions and do not violate any terms from Twitter Developer Agreement and Policy. According to Twitter Policy, we are not allowed to share the entire dataset, but only 100K user IDs. This dataset is available here: https://zenodo.org/record/4618233#.YGGJU2Qzada. The access is open and no approval is required. We provide the directed retweet graph from the Twitter network, all user IDs from the provided retweet graph (89.479 users), all video IDs (vid) extracted from the election related tweets (39.203 video ids) and the directed comment graph.

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