When emotions grow: cross-cultural differences in the role of emotions in the dynamics of conflictual discussions on social media

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

1 Цитирования (Scopus)

Аннотация

Background. The spread of affective content on social media, as well as user grouping based on affect [1], has been a focus of scholarly attention for over a decade. But, despite this, we lack evidence on what roles various particular emotions play in the dynamics of discussions on social media. Emotional contagion theory (Hatfield et al. 2014) adapted for social media suggests that diffusion of emotions happens on individual level, via direct one-time contact with emotionalized content [2]. Other theories, like theories of social influence or social learning [3], thought, suggest multiple, hierarchical, and/or topically-restricted contacts. The idea of affective agenda [4] implies that the dynamics of an emotional discussion needs to be assessed on the aggregate level. The question remains – what role the emotions taken on aggregate level play in the discussion dynamics, being either catalyzers or inhibitors of the discussions. One may suggest that emotions of different stance (positive/negative) may spur/slow down the discussions in various ways. Objectives. We analyze the spread of two polar emotions – anger and compassion – in three Twitter discussions on inter-ethnic conflicts, namely Ferguson protests (the USA, 2014), Charlie Hebdo massacre (France, 2015), and mass harassment in Cologne (Germany, 2015–2016). By analyzing the co-dynamics of the overall discussions and these two emotions we can conclude whether the pattern of the spread of emotions and its link with the discussion dynamics is the same in various language segments of Twitter. Data collection and methods. The data we use were collected by our patented Twitter crawler in the aftermath of the conflicts and include altogether over 2,5 M tweets. We used manual coding by native speakers and machine learning to detect the emotions; then, we visualized the dynamics of growth of the emotional content of the discussions and used Granger test to see whether anger or compassion gave a spur to the discussions. Results. We have received moderate results in terms of the dependence of the number of neutral users upon that of emotional users, but have spotted that the beginnings of the discussions, as well as the discussion outbursts, depend more on compassion, not on angry users, which needs more exploration. We have also shown that the hourly dynamics of emotions replicates that of the larger discussion, and the numbers of angry and compassionate users per hour highly correlate in all the cases.

Язык оригиналаанглийский
Название основной публикацииSocial Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis - 12th International Conference, SCSM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
РедакторыGabriele Meiselwitz
ИздательSpringer Nature
Страницы433-441
Число страниц9
ISBN (печатное издание)9783030495695
DOI
СостояниеОпубликовано - 2020
Событие12th International Conference on Social Computing and Social Media, SCSM 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020 - Copenhagen, Дания
Продолжительность: 19 июл 202024 июл 2020

Серия публикаций

НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том12194 LNCS
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349

конференция

конференция12th International Conference on Social Computing and Social Media, SCSM 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
СтранаДания
ГородCopenhagen
Период19/07/2024/07/20

Предметные области Scopus

  • Теоретические компьютерные науки
  • Компьютерные науки (все)

Ключевые слова

  • Discussion structure
  • Emotion detection
  • Emotional agendas
  • Inter-ethnic discussions
  • Machine learning
  • Twitter

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