Social-Behavioral and Socio-Demographic Analyses

This project explores the connection between linguistic attributes in user-generated English text—spanning informal social text and formal academic text—and socio-demographic factors, including geography, native language family, race, culture, gender, and English proficiency. By employing stylometric, lexical, and semantic analyses, we aim to uncover linguistic patterns that can improve downstream text classification tasks.

The project aims to comprehend text generation (by authors/creators) and reception (by audiences) across creative and academic domains by individuals. It seeks to uncover patterns shaping the reception and impact of textual data, such as factors influencing text popularity in user-generated book reviews, and understanding author behavior in keyphrase selection within scientific articles.

This project analyzes public discourse on socio-political issues through social media data. One study explores U.S. perceptions of illegal immigration on Reddit, revealing polarized attitudes and societal challenges, while another examines global sentiment on the Russia-Ukraine conflict using Twitter, uncovering themes of war criticism, support for Ukraine, and humanitarian concerns. The research highlights how social media analytics can identify key themes, emotional responses, and demographic trends, providing valuable insights to inform policymaking on complex socio-political issues.

Relevant Publication:

[C4] Sazzed, S., Stylometric and Semantic Analysis of Demographically Diverse Non-native English Review Data, In Advances in Social Network Analysis and Mining (ASONAM), 2022.

[C3] Sazzed, S., Influence of Language Proficiency on the Readability of Review Text and Transformer- based Models for Determining Language Proficiency, In SocialNLP@ The Web Conference (WWW), 2022.

[C2] Sazzed, S., Revealing the Demographic Attributes of the Authors from the Abstracts of Scientific Articles, In ACM Conference on Hypertext and Social Media (ACM HT), 2022.

[C1] Sazzed, S. , Comprehending Lexical and Affective Ontologies in the Demographically Diverse Spatial Social Media Discourse, In International Conference on Machine Learning and Applications (ICMLA), 2023.

[J1] Sazzed, S. , Impact of demography on linguistic aspects and readability of reviews and per- formances of sentiment classifiers., In International Journal of Information Management Data Insights, 2022 (Cite Score: 26.5)

Relevant Publication:

[C2] Sazzed, S. , What factors influence the popularity of user-generated text in the creative domain? A case study of book reviews, In International Conference on Machine Learning and Applications (ICMLA), 2023.

[C1] Sazzed, S., An Exploratory Study on the Author Keyphrases Selection Behaviours in Scientific Articles, In Social Computing, Behavioral-Cultural Modeling & Prediction (SBP-BRiMS), 2022.

Relevant Publication:

[C2] Sazzed, S. and Ullah, S., Unmasking Public Perception: A Mixed-Methods Exploration of Social Media Discourse on US Illegal Immigration, In IEEE International Conference on Big Data (BigData), 2024.

[C1] Sazzed, S., The Dynamics of Ukraine-Russian Conflict through the Lens of Demographically Diverse Twitter Data, In IEEE International Conference on Big Data (BigData), 2022.