Doctoral Research
facebook-reactions-covid19-india
Multi-method analysis of Facebook Reactions as emotional indicators of public engagement with COVID-19 news across Indian media platforms (2020–2022).
University of Urbino Carlo Bo · DISCUI · Urbino, Italy
Sawood Anwar
PhD · Humanities (Text & Communication Sciences) · Early Career Researcher
Computational social scientist working at the intersection of natural language processing, social media analysis, and health communication — with a particular focus on how platform-native affordances function as proxies for collective emotion during societal crises in Global South media ecosystems.
IAbout
I hold a Ph.D. in Humanities (Curriculum: Text and Communication Sciences, Cycle XXXVII, GSPS-06/A) from the Department of Communication Sciences, Humanities and International Studies (DISCUI), University of Urbino Carlo Bo, Italy, conferred in September 2025. The programme was coordinated by Prof. Liana Lomiento, supervised by Prof. Fabio Giglietto, and co-supervised by Prof. Giovanni Boccia Artieri.
My doctoral research investigated Facebook Reactions as indicators of public sentiment and engagement with COVID-19 pandemic news in India, employing a mixed-methods framework combining time-series analysis, BERTopic-based topic modelling, LLM-assisted cluster labelling, and lexicon-based sentiment analysis across a corpus of 68,319 Facebook posts (March 2020 – March 2022).
My current research agenda extends this work in two directions: cross-platform comparative studies of affective engagement during public health crises, and the methodological development of LLM-assisted topic interpretation pipelines applicable to low-resource and non-Western media corpora. I am actively seeking collaborations at the intersection of computational methods, platform studies, and health communication.
IIDoctoral Thesis
“Facebook Reactions” as Emotional Indicators: A Multi-Method Approach to Analyzing User Engagement with COVID-19 News on Indian Media Platforms
The thesis employs a mixed-methods approach combining time-series analysis, embedding-based topic modelling, GPT-4-assisted cluster labelling, and lexicon-based sentiment analysis. The primary corpus comprises 68,319 Facebook posts from four major English-language Indian news outlets — The Times of India, The Hindu, The Indian Express, and Hindustan Times — spanning March 24, 2020 to March 31, 2022, with a focused sub-corpus of 8,622 posts for the early lockdown phase (March 24 – April 14, 2020). Findings demonstrate how discrete Reaction types (Like, Love, Haha, Wow, Sad, Angry) function as fine-grained proxies for affective public response, tracking shifts in collective sentiment across dominant news themes and pandemic phases.
The thesis contributes an operationalised framework for treating platform-native reaction affordances as indicators of public affect, offering methodological and substantive advances to computational communication, health communication, and platform studies.
IIIResearch Interests
My work draws on computational and digital methods to examine how platform affordances shape collective emotion, public discourse, and information diffusion — with emphasis on health crises and media ecosystems of the Global South.
- Platform affordances & collective emotion — how reaction mechanisms (Facebook, Instagram, YouTube) encode and aggregate public affect at scale
- Health communication & crisis informatics — audience engagement patterns with pandemic, epidemic, and public health news coverage
- Computational topic modelling — embedding-based (BERTopic) and structural (STM) approaches; LLM-assisted cluster labelling and validation
- Global South media ecosystems — Indian English-language press, multilingual corpora, low-resource NLP applications
- Misinformation & political communication — detection, spread, and audience response on social media platforms
- Cross-platform comparative methods — longitudinal and time-series frameworks for digital engagement data
IVMethods & Tools
- R — tidyverse, quanteda, stm, tidytext, ggplot2, lavaan
- Python — BERTopic, sentence-transformers, scikit-learn
- Methods — topic modelling, sentiment analysis, time-series, content coding
- Data Sources — Meta platforms, CrowdTangle, Meta Content Library API
- LLM Integration — GPT-4-assisted semantic cluster labelling and thematic annotation
VResearch Impact
1
Peer-Reviewed Journal Article
Frontiers in Sociology, 2024
Frontiers in Sociology, 2024
1
Doctoral Dissertation
University of Urbino, 2025
University of Urbino, 2025
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Semantic Scholar Citations
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Crossref Citations
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Google Scholar Profile
Citations & h-index
Citations & h-index
68K+
Facebook Posts Analysed
Across 4 Indian news outlets
Across 4 Indian news outlets
2+
Years of Pandemic Data
March 2020 – March 2022
March 2020 – March 2022
Citation counts via Semantic Scholar & Crossref APIs — updated automatically every Monday. Full metrics & h-index: Google Scholar.
VINews & Updates
August 2025
Invited presentation at Media Sociology Symposium / CITAMS
“Decoding digital emotions: How Facebook reactions reveal public sentiment patterns in Indian COVID-19 news coverage” — Virtual.
September 2025
PhD conferred by University of Urbino Carlo Bo
Doctorate in Humanities (Text & Communication Sciences, Cycle XXXVII) awarded following successful defence.
April 2025
Paper presented at PSA 75th Annual Conference
“Sentiment analysis and topic modeling of COVID-19 news coverage in India” — Birmingham, UK.
2025–2026
Working paper in preparation
Cross-platform affective engagement with health crisis news — extending the doctoral framework to Facebook, Instagram, and YouTube. Contact for preprint.
VIIAcademic Service
Conference Presentations
- PSA 75th Annual Conference — “Sentiment analysis and topic modeling of COVID-19 news coverage in India” (Birmingham, UK, April 2025)
- Media Sociology Symposium / CITAMS — “Decoding digital emotions: How Facebook reactions reveal public sentiment patterns in Indian COVID-19 news coverage” (Virtual, August 2025)
Professional Memberships
- ECREA — European Communication Research and Education Association
- ECPR — European Consortium for Political Research
- AoIR — Association of Internet Researchers
- PSA — Political Studies Association
VIIIResearch Projects & Code
Time-Series Analysis
timeseries-facebook-engagement-r
R workflows for longitudinal digital engagement data — anomaly detection, event-spike analysis, and visualisation.
Topic Modelling
stm-social-media-r
Structural Topic Model (STM) pipeline in R — preprocessing, model fitting, covariate specification, and result interpretation.
Embedding-Based NLP
bertopic-media-topics
BERTopic pipeline for thematic exploration of media corpora using sentence embeddings and hierarchical clustering.
Platform Analysis
meta-content-analysis
Content analysis workflows for platform-mediated communication, misinformation spread, and audience engagement.
Coding Framework
reddit-political-misinfo-coding
Manual coding framework for political communication and misinformation research on Reddit — codebook, reliability checks, and annotation guide.
IXPublications
Facebook Reactions as Emotional Indicators: Analyzing Public Engagement with COVID-19 Pandemic News on Indian Media Platforms During the Early Lockdown Phase
This study treats Facebook Reactions (Like, Love, Haha, Wow, Sad, Angry) as fine-grained proxies for emotional public response to COVID-19 news during India’s early lockdown phase (March–April 2020). Lexicon-based sentiment analysis, embedding-based topic modelling, and time-series techniques are combined to trace how discrete affective signals co-vary with dominant news themes across four major Indian media outlets.
Facebook ReactionsCOVID-19Sentiment AnalysisIndian MediaSocial Media EngagementHealth Communication
“Facebook Reactions” as Emotional Indicators: A Multi-Method Approach to Analyzing User Engagement with COVID-19 News on Indian Media Platforms
A mixed-methods doctoral study examining platform-native affective engagement with COVID-19 news across four major Indian English-language media outlets (March 2020 – March 2022), combining topic modelling, time-series analysis, and LLM-assisted cluster interpretation on a corpus of 68,319 Facebook posts.
Facebook ReactionsCOVID-19Topic ModellingBERTopicTime-Series AnalysisComputational CommunicationIndia
Cross-Platform Affective Engagement with Health Crisis News: A Comparative Study of Reaction Affordances
Extending the doctoral thesis framework to a cross-platform comparative setting, this work examines how reaction affordances on different social media platforms (Facebook, Instagram, YouTube) capture divergent emotional responses to health crisis news coverage.
Cross-Platform AnalysisAffective EngagementHealth CommunicationLLM Topic ValidationGlobal South
XContact
I welcome enquiries from prospective collaborators, journal editors, conference organisers, and researchers working on related questions. Please use the form below or write directly to anwar1524@gmail.com .