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Causal Effects of Trigger Words in Social Media Discussions: A Large-Scale Case Study about UK Politics on Reddit

Dimosthenis Antypas, Christian Arnold, Nedjma Ousidhoum, Carla Perez Almendros, Jose Camacho-Collados

Abstract

Political debates on social media often escalate quickly, leading to increased engagement as well as more emotional and polarised exchanges. Trigger points (Mau, Lux, and Westheuser 2023) represent moments when individuals feel that their understanding of what is fair, normal, or appropriate in society is being questioned. Analysing Reddit discussions, we examine how trigger points shape online debates and assess their impact on engagement and affect. Our analysis is based on over 100 million comments from subreddits centred on a predefined set of terms identified as trigger words in UK politics. We find that mentions of these terms are associated with higher engagement and increased animosity, including more controversial, negative, angry, and hateful responses. These results position trigger words as a useful concept for modelling and analysing online polarisation.

Causal Effects of Trigger Words in Social Media Discussions: A Large-Scale Case Study about UK Politics on Reddit

Abstract

Political debates on social media often escalate quickly, leading to increased engagement as well as more emotional and polarised exchanges. Trigger points (Mau, Lux, and Westheuser 2023) represent moments when individuals feel that their understanding of what is fair, normal, or appropriate in society is being questioned. Analysing Reddit discussions, we examine how trigger points shape online debates and assess their impact on engagement and affect. Our analysis is based on over 100 million comments from subreddits centred on a predefined set of terms identified as trigger words in UK politics. We find that mentions of these terms are associated with higher engagement and increased animosity, including more controversial, negative, angry, and hateful responses. These results position trigger words as a useful concept for modelling and analysing online polarisation.
Paper Structure (26 sections, 6 figures, 7 tables)

This paper contains 26 sections, 6 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Term selection
  5. Data collection
  6. Research design
  7. Control groups
  8. Statistical analysis
  9. Features
  10. User engagement (Hypothesis 1)
  11. Animosity (Hypothesis 2)
  12. Results
  13. User engagement (Hypothesis 1)
  14. Animosity (Hypothesis 2)
  15. Robustness of Results
  16. ...and 11 more sections

Figures (6)

  • Figure 1: Consequences of trigger words. Hypothesis 1: Trigger words cause more engagement; Hypothesis 2: Trigger words cause more animosity.
  • Figure 2: Example: Distribution of comments in the treatment and control groups (space) for the trigger word Rwanda.
  • Figure 3: Average controversiality per thread for the treatment and control (space) groups of Rwanda.
  • Figure 4: Rwanda Treatment and Control distributions of comments in the Contextual and Temporal settings.
  • Figure 5: Placebo treatments every 10 minutes, ranging from 30 minutes before to 30 minutes after the actual treatment point.
  • ...and 1 more figures