Covid-19 and 5G

Conspiracy theories during and after the pandemic

Francesco Iarlori, Armando Leotta, Adriano Oliveto, Domingo Scisci

2025-01-23

Introduction

The Transformative Impact of COVID-19

  • COVID-19 has created a historic societal rift, profoundly affecting public health, social, economic, and cultural structures globally.
  • It is not just a health crisis but also a social and economic crisis of unprecedented scale - Alon et al. (2020).
  • The pandemic has exposed and exacerbated pre-existing social inequalities, disproportionately impacting disadvantaged groups - Bambra et al. (2020).
  • Key effects:
    • Acceleration of technological changes (e.g., teleworking, distance learning).
    • Emergence of the “digital divide,” highlighting issues of access and equity - De’, Pandey, and Pal (2020).
  • Raised questions about government-citizen relationships, public safety, and individual freedoms.

COVID-19 and Social Resilience

  • COVID-19 provided an opportunity to explore “social resilience,” defined as the ability of communities to adapt and transform in response to crises - keck and Sakdapolrak (2013).
  • Beyond recovery, resilience includes transitioning to more equitable and sustainable systems.
  • Pandemic as a catalyst for long-term societal changes, challenging traditional sociological theories.
  • Offers insights into interactions between health, society, and politics, with tools to prepare for future crises.

Research Questions

  1. What remains of the relationship between COVID-19 and conspiracy theories (e.g., 5G)?
  2. What are the most widespread sentiments and emotions within conspiracy-related discussions?
  • Example: The association between COVID-19 and 5G misinformation peaked in April 2020.

Google Trends: Covid19 5G (Jan - Jul 2020)

Methodology

Previous Analysis: UK Twitter Data

  • Focused on tweets from the early months of the pandemic in the UK.
  • Revealed a conspiratorial core:
    • Beliefs of “powerful elites” controlling the pandemic.
    • COVID-19 as “fake news” hiding dominant groups’ true interests.
  • Network Analysis: The conspiratorial core is highlighted (see image).

5G Conspiracy Theories: From Twitter to Reddit

  • Previous analysis on Twitter revealed a strong connection between COVID-19 and 5G conspiracy theories during the pandemic.
  • Current research examines whether these trends persist on Reddit, offering a new platform and audience for analysis.
  • Research focus: Investigate ongoing relevance and emotional drivers of conspiracy narratives.

Why Study Conspiracies on Reddit?

  • Unique Platform Dynamics:
    • Subreddits foster highly engaged and ideologically homogeneous communities.
    • Decentralized structure creates echo chambers, amplifying conspiracy theories.
    • Voting system prioritizes popular content, enabling rapid dissemination of narratives.
  • Semi-anonymous nature:
    • Encourages open, honest discussions.
    • Provides a rich dataset for analyzing the evolution and social impact of conspiratorial discourse.

Methodology: Sentiment and Emotion Analysis

  • Sentiment Analysis:
    • Classifies text as positive, negative, or neutral.
    • Simplifies opinions and attitudes into binary or ternary categories - Liu (2012).
    • Useful for monitoring online tone or consumer feedback.
  • Emotion Analysis:
    • Identifies specific emotions in text (e.g., anger, joy, sadness, fear).
    • Provides nuanced insights into emotional drivers of content - Mohammad and Turney (2013).

Data Scraping Process

  • Target Subreddit: r/conspiracy
    • Focused on conspiracy theories and secret societies.
    • Provides a rich dataset for examining conspiratorial discourse.
  • Tools Used:
    • PRAW library: Python wrapper for Reddit’s API.
    • Custom functions:
      • get_thread_list(): Retrieves a list of threads.
      • get_thread_comments(): Collects all comments from selected threads.

Dataset Excerpt

thread_id thread_title thread_author comment_id parent_id author text date clean_text sentiment emotion
0 130qa8d The “flat earth” conspiracy theory is intentio... raf_lapt0p jhxeksf t3_130qa8d AutoModerator ###[Meta] Sticky Comment\n\n[Rule 2](https://w... 2023-04-27 15:07:45 sticky comment rule apply reply stickie commen... neutral neutral
1 130qa8d The “flat earth” conspiracy theory is intentio... raf_lapt0p jhzsd9e t1_jhxeksf dcforce See what you are missing because of the Globe ... 2023-04-28 00:49:20 miss globe pace apologist negative neutral
2 130qa8d The “flat earth” conspiracy theory is intentio... raf_lapt0p jhxeulc t3_130qa8d WittyNameNo2 Should we be calling this the “flat earth cons... 2023-04-27 15:09:35 call flat earth conspiracy theory conspiracy t... neutral neutral
3 130qa8d The “flat earth” conspiracy theory is intentio... raf_lapt0p ji2y3w5 t1_jhxeulc vld138 I don't think this is a good idea..but in your... 2023-04-28 17:46:38 think good idea theory ok believe positive approval
4 130qa8d The “flat earth” conspiracy theory is intentio... raf_lapt0p jhynp63 t1_jhxeulc Emergency_Sandwich_6 I think the actual conspiracy is that space ob... 2023-04-27 20:01:14 think actual conspiracy space object actually ... neutral neutral

Analysis

Comment Activity Over Time

Comment Activity Over Time

  • Key Findings:
    • Peaks:
      • Early spike in May 2020, driven by heightened interest in conspiracy theories during the pandemic’s onset.
      • Subsequent surges: Late 2021, early 2022, and April 2023, likely tied to specific events reigniting discourse.
    • Sustained Engagement: Activity levels remain steady, showing persistent community interest in conspiratorial narratives.

Comment Length Distribution

Comment Length Distribution

  • Most comments are short:
    • Majority under 500 characters, reflecting concise exchanges (opinions, reactions).
  • Longer comments present:
    • A smaller number exceed 1,000 characters, suggesting detailed arguments or rebuttals.
  • Takeaway: Subreddit is driven by quick interactions but allows for deeper discourse.

Sentiment Analysis: Methods

  • Tool Used:
  • Implementation:
    • Used Python’s transformers library to classify comments as positive, negative, or neutral.
  • Output: Each comment was assigned a sentiment label for further analysis.

Sentiment Distribution

  • Neutral Sentiments (53.8%): Predominantly factual or objective tones.
  • Negative Sentiments (35.8%): Criticism, frustration, or disagreement, reflecting the contentious nature of conspiracy discussions.
  • Positive Sentiments (10.3%): Limited expressions of support or optimism.
  • Conclusion: The subreddit leans heavily towards neutral and negative tones.

Sentiment Analysis for High-Engagement Threads

Sentiment Analysis for High-Engagement Threads

  • Patterns Mirror Overall Trends:
    • Neutral sentiments dominate.
    • Negative comments closely follow.
    • Positive comments remain minimal, even in threads with 100+ comments.
  • Insight: Popular threads reflect polarized and critical discussions rather than collaborative or positive discourse.

TF-IDF and Wordcloud Analysis

TF-IDF and Wordcloud Analysis

  • TF-IDF Method:
    • Highlights key terms in comments based on their frequency and uniqueness within the corpus.
  • Key Findings:
    1. Positive Sentiments: Words like “thank,” “great,” and “love” reflect appreciation and curiosity.
    2. Negative Sentiments: Terms such as “shit,” “bad,” and “vaccine” reflect frustration and polarized topics.
    3. Neutral Sentiments: Common words like “people,” “think,” and “comment” suggest rational or argumentative tones.
  • Conclusion: Wordclouds capture the subreddit’s diverse tone, from emotional expressions to rational exchanges.

Emotion Analysis: Methodology

Emotion Distribution

Emotion Distribution

  • Neutral Emotions:
    • Dominant category with 10,183 instances, indicating most discussions are factual or emotionally neutral.
  • Top Positive Emotions:
    • Amusement (606), admiration (515), and gratitude (419) show engagement and acknowledgment.
  • Top Negative Emotions:
    • Annoyance (403) and anger (257) highlight frustration and critical tones.
  • Less Common Emotions:
    • Sadness (169), joy (124), and surprise (90) suggest limited extreme emotional expressions.

Key Insights from Emotion Analysis

  • Neutrality dominates overall, reflecting the subreddit’s focus on factual or emotionally subdued exchanges.
  • Positive emotions emphasize acknowledgment and engagement, with limited humor or happiness.
  • Negative emotions highlight irritation and critique, with less emphasis on deep negativity.
  • Comparison underscores the contrasting emotional drivers of positive (admiration) and negative (annoyance) comments.

Conclusions

Key Insights

  • Dynamics of Misinformation:
    • Conspiracy theories persist across platforms, with Reddit fostering thematic discussions in echo chambers and Twitter amplifying viral, concise statements.
  • Relevance to Social Research:
    • Social media data provides a rich resource for understanding misinformation and its societal impact.
  • Platform Differences:
    • Future research must account for differences in audience and engagement modes to ensure valid comparisons.

Challenges in Data Collection

  • Resource-Intensive Process:
    • High computational and operational demands.
    • API limitations restrict data volume and temporal access.
  • Data Preparation:
    • Raw data cleaning is time-consuming and costly.
  • Need for Solutions:
    • Optimized scraping tools and academic-focused APIs are essential.

Ethical Considerations

  • Privacy Concerns:
    • Even publicly available comments may inadvertently expose sensitive user information.
  • Adherence to Data Protection Regulations:
    • Anonymization and GDPR compliance are critical.
  • Social Responsibility:
    • Avoid stigmatizing communities or reinforcing biases.
    • Transparency in reporting findings is essential to maintain trust.

Future Research Directions

  • Expanded Scope:
    • Analyze longer time frames and multiple platforms for broader representativeness.
    • Compare platforms to explore demographic and socio-cultural influences on discussions.
  • Narrative Evolution:
    • Study how sentiment, emotions, and conspiracy theories evolve over time.

Dashboard

Available at the following link: Covid-19 and 5G

References

Alon, Titan, Matthias Doepke, Jane Olmstead-Rumsey, and Michèle Tertilt. 2020. “The Impact of COVID-19 on Gender Equality.” https://doi.org/10.3386/w26947.
Bambra, Clare, Ryan Riordan, John Ford, and Fiona Matthews. 2020. “The COVID-19 Pandemic and Health Inequalities.” J Epidemiol Community Health 74 (11): 964–68. https://doi.org/10.1136/jech-2020-214401.
De’, Rahul, Neena Pandey, and Abhipsa Pal. 2020. “Impact of Digital Surge During Covid-19 Pandemic: A Viewpoint on Research and Practice.” International Journal of Information Management, Impact of COVID-19 pandemic on information management research and practice: Editorial perspectives, 55 (December): 102171. https://doi.org/10.1016/j.ijinfomgt.2020.102171.
keck, Markus, and Patrick Sakdapolrak. 2013. “What Is Social Resilience? Lessons Learned and Ways Forward.” Erdkunde 67 (1): 5–19. https://www.jstor.org/stable/23595352.
Liu, Bing. 2012. Sentiment Analysis and Opinion Mining. San Rafael: Morgan & Claypool Publishers.
Mohammad, Saif M., and Peter D. Turney. 2013. “Crowdsourcing a WordEmotion Association Lexicon.” Computational Intelligence 29 (3): 436–65. https://doi.org/10.1111/j.1467-8640.2012.00460.x.