Behavioral-Cultural Modeling and Prediction

This project explores the influence of demographic factors (geography, native language family, race, culture, and English proficiency levels) on non-native English text attributes across distinct groups. Through stylometric, lexical, and semantic analysis, we aim to uncover differences in linguistic characteristics among different groups that can help to improve various 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 study focuses on understanding the social media discourse related to various events such as dynamics of the Ukraine-Russian conflict using demographically diverse Twitter data. This involves various aspects such as semantic analysis, topic modeling, among others.

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: 10.5 

* new journal, IF not available yet.

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:

[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.