Specialisation: Social Media Analysis · Topic Modelling · Sentiment Analysis · Health Communication · Computational Communication

🔍 Open to: Postdoctoral positions · Research fellowships · Visiting researcher roles · Collaborative projects in computational communication, NLP, social media analysis, and digital methods.

I am a computational social scientist and early career researcher whose work sits at the intersection of natural language processing, social media analysis, and computational communication science. I hold a PhD from the University of Urbino Carlo Bo, Italy (Cycle XXXVII, 2025), where I was supervised by Prof. Fabio Giglietto and co-supervised by Prof. Giovanni Boccia Artieri.

My doctoral research examined Facebook Reactions as emotional indicators of public sentiment and engagement with COVID-19 news on Indian media platforms (March 2020 – March 2022), drawing on a dataset of 68,319 Facebook posts from four major English-language outlets. The methodology integrated time-series analysis, BERTopic embedding-based topic modelling, LLM-assisted cluster labelling, and lexicon-based sentiment analysis.

Beyond my dissertation, my research agenda spans health communication, misinformation detection, platform studies, crisis communication, and psychometric scale validation. My published work has received citations tracked live below.

"Facebook Reactions" as Emotional Indicators: A Multi-Method Approach to Analyzing User Engagement with COVID-19 News on Indian Media PlatformsSawood Anwar — University of Urbino Carlo Bo, 2025 🔗 Full text — ORA Uniurb Institutional Repository

The thesis investigates how discrete Facebook Reaction types (Like, Love, Haha, Wow, Sad, Angry) function as affective engagement signals tracking shifting public sentiment across dominant news themes and pandemic phases in India. The dataset includes a focused subset of 8,622 posts covering the early pandemic phase (March 24 – April 14, 2020) alongside the full longitudinal corpus.

Keywords:Facebook ReactionsCOVID-19Indiasentiment analysisBERTopictopic modellingtime-serieshealth communicationcomputational communication

[1]Anwar, S., & Giglietto, F. (2024). Facebook Reactions as Emotional Indicators: Analyzing Public Engagement with COVID-19 Pandemic News on Indian Media Platforms During the Early Lockdown Phase. Frontiers in Sociology, 9, 1379265. 🔗 DOI: 10.3389/fsoc.2024.1379265 · 🔓 Open Access

[2] Giglietto, F., Ghasiya, P., Sasahara, K., Anwar, S., & Mincigrucci, R. (2022). Between Localism and Politics: Mapping Coordinated Networks that Circulate Problematic Health Content in India. SSRN Working Paper 4164140. 🔗 DOI: 10.2139/ssrn.4164140 · View on SSRN / Google Scholar

Live counts fetched weekly from Semantic Scholar and Crossref APIs via GitHub Actions. Source data: citations.json · Updated every Monday at 06:00 UTC

Source	Live Count	DOI
		10.3389/fsoc.2024.1379265
		10.3389/fsoc.2024.1379265
	View profile →	Full metrics & h-index

• Computational Communication Science — platform-native engagement signals, affective public response, digital media effects
• Natural Language Processing — topic modelling (BERTopic, STM), text embeddings, LLM-assisted annotation
• Sentiment & Affective Analysis — lexicon-based methods, reaction-type classification, emotion detection
• Health & Crisis Communication — COVID-19, pandemic misinformation, Indian media platforms
• Disinformation & Platform Studies — coordinated inauthentic behaviour, cross-platform analysis
• Quantitative & Computational Methods — time-series, ML classification, survey experiments, psychometric scale validation

Open Science Commitment: All code in this repository is publicly available under open licences to support reproducibility and transparency. Primary research data is governed by Meta's CrowdTangle and Content Library data-use agreements.

RPythonFacebookCOVID-19IndiaNLPBERTopicSentiment AnalysisTime-Series

Core repository for the doctoral dissertation. Multi-method analysis of Facebook Reactions (68,319 posts, 2020–2022) as emotional indicators of public engagement with COVID-19 news across The Times of India, The Hindu, Indian Express, and Hindustan Times. 📝 Linked publication:Frontiers in Sociology, 2024

RTime-SeriesFacebookAnomaly DetectionSocial MediaCOVID-19

Reproducible R framework for longitudinal social media engagement research: a general-purpose time-series toolkit, a COVID-19 Facebook extension with reaction-type and pandemic-phase stratification, and a health misinformation spike-detection module with event annotation.

RSTMSocial MediaNLPReproducible Research

Reproducible Structural Topic Model (STM) pipeline for social media corpora. Covers corpus construction, DFM building, searchK model selection, prevalence covariate specification, estimateEffect inference, and publication-quality visualisation.

PythonRBERTopicSentence TransformersUMAPHDBSCANLLM

BERTopic pipeline for thematic analysis of media corpora: sentence embeddings → UMAP reduction → HDBSCAN clustering → c-TF-IDF topic representations → LLM-based semantic labelling. Outputs exported to R for post-hoc engagement analysis.

RSentiment AnalysisAFINNBingNRCLexicon

Systematic comparison of AFINN, Bing, and NRC lexicons applied to social media text, with annotated code and an interpretive guide to lexicon selection for computational communication research.

RFacebookInstagramHealth MisinformationSTM

Content analysis workflows for studying platform-mediated health communication and misinformation on Meta platforms, combining STM topic modelling with engagement metric analysis.

RRedditContent CodingIntercoder ReliabilityMisinformation

Manual content coding framework for political communication and misinformation research on Reddit. Includes codebook, intercoder reliability scripts (Krippendorff's α, Cohen's κ), and descriptive analysis pipeline.

RFacebookInstagramRedditCross-Platform

Unified R framework harmonising engagement data from Facebook, Instagram, and Reddit into a single comparative schema for cross-platform digital communication research.

PythonRMachine LearningDisinformationSHAPTF-IDFExplainable AI

Supervised ML pipeline benchmarking Logistic Regression, Random Forest, SVM, and Naïve Bayes for disinformation classification in news posts, with SHAP-based model interpretability.

RtidymodelstextrecipesNews Classification

Supervised text classification pipeline in R for labelling news articles by topic (health, politics, economy) and credibility using tidymodels and textrecipes.

PythonRCrowdTangleMeta Content Library APIResearch Ethics

Documented academic data collection pipeline for Facebook and Instagram research. Covers legacy CrowdTangle exports and Meta Content Library API access, including ethics guidelines and a unified data schema.

RSurvey DataLikert ScalessrvyrgtsummaryQualtrics

End-to-end survey data analysis pipeline: import, cleaning, Likert scale analysis, descriptive statistics, survey weighting, and diverging bar chart visualisation. Compatible with Qualtrics, SurveyMonkey, SPSS, and Stata exports.

RpsychlavaanEFACFAPsychometricsSEM

Psychometric scale validation using psych and lavaan: item-level inspection, reliability (Cronbach's α, McDonald's ω), EFA with parallel analysis, CFA with fit indices (CFI, TLI, RMSEA, SRMR), and convergent/discriminant validity.

RSurvey ExperimentsVignette StudiesestimatrCausal Inference

Analysis pipeline for survey experiments and vignette studies: randomisation checks, ATE estimation with robust standard errors, OLS regression with modelsummary tables, and moderation/interaction effect visualisation.

Sawood Anwar · PhD · Computational Social Scientist · University of Urbino Carlo Bo, Italy · 2025