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
Sawood Anwar
PhD in Humanities (Text & Communication Sciences) · Early Career Researcher
Computational social scientist working at the intersection of natural language processing, social media analysis, and computational communication. My research investigates how digital platforms mediate public emotion and information during societal crises.
IAbout
I am an early career researcher working at the intersection of computational communication, social media analysis, and natural language processing. I hold a Ph.D. in Humanities (Curriculum: Text and Communication Sciences, Cycle XXXVII) from the Department of Communication Sciences, Humanities and International Studies (DISCUI), University of Urbino Carlo Bo, Italy, defended in September 2025. My doctoral programme was coordinated by Prof. Liana Lomiento, supervised by Prof. Fabio Giglietto, and co-supervised by Prof. Giovanni Boccia Artieri.
My doctoral research examined Facebook Reactions as emotional indicators of public engagement with COVID-19 pandemic news on Indian media platforms (March 2020 – March 2022). Using a multi-method approach — time-series analysis, embedding-based topic modelling, a Large Language Model (LLM)-driven semantic synthesis approach for topic cluster interpretation, and lexicon-based sentiment analysis — I analysed a corpus of 68,319 Facebook posts from four major English-language Indian news outlets, with a focused subset of 8,622 posts for the early pandemic phase.
IIDoctoral Thesis
“Facebook Reactions” as Emotional Indicators: A Multi-Method Approach to Analyzing User Engagement with COVID-19 News on Indian Media Platforms
This thesis investigates the role of Facebook Reactions as indicators of public sentiment and engagement with COVID-19 pandemic-related news in India during different stages of the pandemic, ranging from March 24, 2020 to March 31, 2022. The work employs a mixed-methods approach combining time-series analysis with embedding-based topic modelling, GPT-4-assisted cluster labelling, and lexicon-based sentiment analysis to examine a dataset of 68,319 Facebook posts and a focused subset of 8,622 posts analysed for the early stages of the pandemic (March 24 – April 14, 2020) to capture initial public responses.
Data is derived from four major English-language Indian news outlets: The Times of India, The Hindu, Indian Express, and Hindustan Times. The thesis contributes an operationalised framework for treating platform-native reaction affordances as fine-grained proxies for public affect, offering methodological and substantive contributions to the fields of computational communication, health communication, and platform studies.
Supervisors: Prof. Fabio Giglietto (Supervisor) · Prof. Giovanni Boccia Artieri (Co-Supervisor) · Coordinator: Prof. Liana Lomiento
IIIResearch Interests
My work sits within computational communication research, drawing on methods from NLP and data science to study how platform affordances shape collective emotion, public discourse, and information diffusion — with a particular focus on health crises and the Global South.
Methods & Tools
- R — tidyverse, quanteda, stm, tidytext, psych, lavaan, ggplot2
- Python — scikit-learn, BERTopic, sentence-transformers, NLP pipelines
- Methods — topic modelling, sentiment analysis, time-series, content coding, survey analysis
- Data Sources — Meta platforms, Reddit, CrowdTangle, Meta Content Library API
- LLM Integration — LLM-driven semantic synthesis for topic cluster interpretation and thematic annotation
IVResearch 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
—
Semantic Scholar Citations
Auto-updated weekly
Auto-updated weekly
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 count via Semantic Scholar API — updated automatically every Monday.
VAcademic Service
Conferences & Talks
- 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)
- Memberships — ECREA, ECPR, AoIR, PSA
VIResearch Projects & Code
Time-Series Analysis
timeseries-facebook-engagement-r
Reusable R workflows for digital engagement data — anomaly detection, event-spike analysis, and longitudinal visualisation.
Topic Modelling
stm-social-media-r
Structural Topic Model (STM) workflow in R — preprocessing, model fitting, covariate specification, and result interpretation.
Embedding-Based NLP
bertopic-media-topics
BERTopic-based pipeline for thematic exploration of media corpora using sentence embeddings and hierarchical clustering.
Platform Analysis
meta-content-analysis
Content analysis workflows for studying 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.
VIISelected Publications
Facebook Reactions as Emotional Indicators: Analyzing Public Engagement with COVID-19 Pandemic News on Indian Media Platforms During the Early Lockdown Phase
This study investigates Facebook Reactions as proxies for emotional public response to COVID-19 pandemic news shared by major Indian media outlets during the early lockdown phase (March–April 2020). Combining lexicon-based sentiment analysis with embedding-based topic modelling and time-series techniques, the analysis reveals how discrete reaction types — Like, Love, Haha, Wow, Sad, Angry — track shifting public affect across dominant news themes.
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
This thesis investigates Facebook Reactions as indicators of public sentiment and engagement with COVID-19 news in India (March 2020 – March 2022). A mixed-methods approach combines time-series analysis, embedding-based topic modelling, LLM-driven semantic cluster labelling, and lexicon-based sentiment analysis across a corpus of 68,319 Facebook posts from four major English-language Indian news outlets, with a focused subset of 8,622 posts for the early pandemic phase.
Facebook ReactionsCOVID-19Topic ModellingBERTopicTime-Series AnalysisComputational CommunicationIndia
VIIIAcademic Profiles
IXContact
Whether you are a fellow researcher, a prospective collaborator, a journal editor, or simply curious about my work — I welcome every message. Write to me and I will get back to you as soon as possible.