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

PLOS ONE RESEARCH ARTICLE What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020 Alexander Shevtsov ID1,2, Maria Oikonomidou ID1,2, Despoina Antonakaki ID1*, Polyvios Pratikakis1,2, Sotiris Ioannidis1,3 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 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 OPEN ACCESS Citation: Shevtsov A, Oikonomidou M, Antonakaki D, Pratikakis P, Ioannidis S (2023) What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020. PLoS ONE 18(1): e0270542. https://doi.org/10.1371/journal.pone.0270542 Editor: Daswin De Silva, La Trobe University Melbourne Campus: La Trobe University, AUSTRALIA Received: December 11, 2020 Accepted: May 30, 2022 Published: January 31, 2023 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0270542 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. Data Availability Statement: In order to obtain the dataset used for the analysis described in this study, we follow the Twitter API restrictions and do 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. 1 Introduction Previous studies analyzing political content on Social Networks, mainly focus on a single platform, however, online political campaigns and interactions between users seem to take place across diverse online social networks. Online discourse is scattered across several social networks, sometimes among the same users and communities. The parallel study of the content of these social networks can reveal the political campaigns [1], comments, opinions, PLOS ONE | https://doi.org/10.1371/journal.pone.0270542 January 31, 2023 1 / 31 PLOS ONE 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. 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. Competing interests: The authors have declared that no competing interests exist. #USelections 2020 analysis on Twitter and YouTube tendencies, and beliefs of the electorate [2, 3]. For instance, social media played a very important role during the 2016 US elections [1, 4–6]. However, only a few studies focus on more than one social networks at a time [7, 8], thus possibly missing important interactions. Users on Twitter and YouTube form tight communities. Twitter homophily has been noticed in communities [9], in hashtags [10], on Twitter lists [11], and modelled in the follow and mention graphs [12, 13]. The analysis of this phenomenon has been studied under the prism of political context as well, like in [14–19], or support homophily between users and social media [20]. Homophily and forming of common communities on social media have been noticed on YouTube as well, as seen in several background studies [21–23]. Social media users with political interests share and seek information about politics on Twitter and there is a high probability these particular users will also search for additional information on other social networks with similar topics. This could indicate potential connections across different social platforms, especially in the case of political discourse. Consequently, the analysis of a single social network cannot reveal the entire picture of the connections across several social networks. As supported above, online social networks allow users to form groups and communities, where the members are related to the same topic or interest. Based on that, we assume that communities are generated based on the users’ preferences. Each social network has its own communities, where the users are already connected to each other, by some hidden layers of each social network. In our study, we provide an analysis of Twitter and YouTube where these layers are consisted of t (...truncated)