Self-disclosure on Twitter During the COVID-19 Pandemic: A Network Perspective

Prasanna Umar, Chandan Akiti, Anna Squicciarini, Sarah Rajtmajer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Amidst social distancing, quarantines, and everyday disruptions caused by the COVID-19 pandemic, users’ heightened activity on online social media has provided enhanced opportunities for self-disclosure. We study the incidence and the evolution of self-disclosure temporally as important events unfold throughout the pandemic’s timeline. Using a BERT-based supervised learning approach, we label a dataset of over 31 million COVID-19 related tweets for self-disclosure. We map users’ self-disclosure patterns, characterize personal revelations, and examine users’ disclosures within evolving reply networks. We employ natural language processing models and social network analyses to investigate self-disclosure patterns in users’ interaction networks as they seek social connectedness and focused conversations during COVID-19 pandemic. Our analyses show heightened self-disclosure levels in tweets following the World Health Organization’s declaration of pandemic worldwide on March 11, 2020. We disentangle network-level patterns of self-disclosure and show how self-disclosure characterizes temporally persistent social connections. We argue that in pursuit of social rewards users intentionally self-disclose and associate with similarly disclosing users. Finally, our work illustrates that in this pursuit users may disclose intimate personal health information such as personal ailments and underlying conditions which pose privacy risks.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationApplied Data Science Track - European Conference, ECML PKDD 2021, Proceedings
EditorsYuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano
PublisherSpringer Science and Business Media Deutschland GmbH
Pages271-286
Number of pages16
ISBN (Print)9783030865139
DOIs
StatePublished - 2021
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 - Virtual, Online
Duration: Sep 13 2021Sep 17 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12978 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021
CityVirtual, Online
Period9/13/219/17/21

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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