Negativity spreads faster: A large-scale multilingual twitter analysis on the role of sentiment in political communication

被引:7
|
作者
Antypas, Dimosthenis [1 ]
Preece, Alun [1 ]
Camacho-Collados, Jose [1 ]
机构
[1] Cardiff NLP, Sch Comp Sci & Informat, Cardiff CF24 3AA, Wales
来源
关键词
Politics; Twitter; NLP; POPULARITY;
D O I
10.1016/j.osnem.2023.100242
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Social media has become extremely influential when it comes to policy making in modern societies, especially in the western world, where platforms such as Twitter allow users to follow politicians, thus making citizens more involved in political discussion. In the same vein, politicians use Twitter to express their opinions, debate among others on current topics and promote their political agendas aiming to influence voter behaviour. In this paper, we attempt to analyse tweets of politicians from three European countries and explore the virality of their tweets. Previous studies have shown that tweets conveying negative sentiment are likely to be retweeted more frequently. By utilising state-of-the-art pre-trained language models, we performed sentiment analysis on hundreds of thousands of tweets collected from members of parliament in Greece, Spain and the United Kingdom, including devolved administrations. We achieved this by systematically exploring and analysing the differences between influential and less popular tweets. Our analysis indicates that politicians' negatively charged tweets spread more widely, especially in more recent times, and highlights interesting differences between political parties as well as between politicians and the general population.
引用
收藏
页数:15
相关论文
共 50 条
  • [31] A Large-scale Analysis of Regional Tendency of Twitter Photos Using Only Image Features
    Nagano, Tetsuya
    Ege, Takumi
    Shimoda, Wataru
    Yanai, Keiji
    [J]. 2019 2ND IEEE CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2019), 2019, : 185 - 188
  • [32] User Engagement with Scholarly Twitter Mentions: A Large-scale and Cross-disciplinary Analysis
    Fang, Zhichao
    [J]. 18TH INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS (ISSI2021), 2021, : 387 - 392
  • [33] Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data
    Jamal, Nasir
    Chen, Xianqiao
    Aldabbas, Hamza
    [J]. FUTURE INTERNET, 2019, 11 (09)
  • [34] Towards Positivity: A Large-Scale Diachronic Sentiment Analysis of the Humanities and Social Sciences in China
    Xiao, Wei
    Guo, Yuxin
    Zhao, Xi
    [J]. FUDAN JOURNAL OF THE HUMANITIES AND SOCIAL SCIENCES, 2023, 16 (04) : 569 - 589
  • [35] A Large-Scale Implementation Using MapReduce-Based SVM for Tweets Sentiment Analysis
    Lijo, V. P.
    Seetha, Hari
    [J]. INTELLIGENT COMPUTING AND COMMUNICATION, ICICC 2019, 2020, 1034 : 541 - 549
  • [36] Towards Positivity: A Large-Scale Diachronic Sentiment Analysis of the Humanities and Social Sciences in China
    Wei Xiao
    Yuxin Guo
    Xi Zhao
    [J]. Fudan Journal of the Humanities and Social Sciences, 2023, 16 : 569 - 589
  • [37] Large-Scale Computerized Text Analysis in Political Science: Opportunities and Challenges
    Wilkerson, John
    Casas, Andreu
    [J]. ANNUAL REVIEW OF POLITICAL SCIENCE, VOL 20, 2017, 20 : 529 - 544
  • [38] TrendMiner: Large-Scale Analysis of Political Attitudes in Public Facebook Messages
    Mihaltz, Marton
    Varadi, Tamas
    [J]. 2015 6TH IEEE INTERNATIONAL CONFERENCE ON COGNITIVE INFOCOMMUNICATIONS (COGINFOCOM), 2015, : 265 - 265
  • [39] Visualization, documentation, analysis, and communication of large-scale gene regulatory networks
    Longabaugh, William J. R.
    Davidson, Eric H.
    Bolouri, Hamid
    [J]. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS, 2009, 1789 (04): : 363 - 374
  • [40] LARGE-SCALE NETWORK ANALYSIS WITH APPLICATIONS TO TRANSPORTATION, COMMUNICATION AND INFERENCE NETWORKS
    TEH, HH
    FOO, MF
    [J]. DISCRETE MATHEMATICS, 1988, 72 (1-3) : 347 - 353