Department of Computer Science and Engineering
Indian Institute of Technology Kharagpur
Supervisor: Prof. Niloy Ganguly
Projects:
Publications:
Supervisor: Animesh Mukherjee, Pawan Goyal
Projects:
Spread of hate speech in online social media : Understand how hate speech spread in online social media sites.
Thou shalt not hate: Countering online hate speech: Provides dataset and analysis of different counter strategies used for hate speech.
Interaction dynamics between hate and counter users on Twitter: Characterization of hate and counter users in Twitter
Temporal effects of Unmoderated Hate speech in Gab : Understand the temporal effects of hate speech in online social media
Publications:
Spread of hate speech in online social media : Understand how hate speech spread in online social media sites.
Thou shalt not hate: Countering online hate speech: https://github.com/binny-mathew/Countering_Hate_Speech_ICWSM2019
Interaction dynamics between hate and counter users on Twitter:https://github.com/binny-mathew/Spread_Hate_Speech_WebSci19
Temporal effects of Unmoderated Hate speech in Gab
Supervisor: Animesh Mukherjee
Projects:
Publications:
Supervisor: Dr. Sourangshu Bhattacharya
Projects:
Travel Time Prediction
Subset selection on Autonomous Vehicular Data
Publications:
Supervisor: Animesh Mukherjee, Pawan Goyal
Projects:
Aspect-based Sentiment Analysis of Scientific Reviews
Bias in Indian Media
Automated MIDI file Generation from songs
Publications:
Supervisor: Niloy Ganguly
Projects:
Popularity Prediction of Hashtags
Predicting the popularity dynamics of Twitter hashtags has a broad spectrum of applications. Existing works have primarily focused on modeling the popularity of individual tweets rather than the underlying hashtags. As a result, they fail to consider several realistic factors contributing to hashtag popularity. In this paper, we propose Large Margin Point Process (LMPP), a probabilistic framework that integrates hashtag-tweet influence and hashtaghashtag competitions, the two factors which play important roles in hashtag propagation. Furthermore, while considering the hashtag competitions, LMPP looks into the variations of popularity rankings of the competing hashtags across time. Extensive experiments on seven real datasets demonstrate that LMPP outperforms existing popularity prediction approaches by a significant margin. Additionally, LMPP can accurately predict the relative rankings of competing hashtags, offering additional advantage over the state-of-the-art baselines.
Publications:
LMPP: A Large Margin Point Process Combining Reinforcement and Competition for Modeling Hashtag Popularity., IJCAI 2017
Strm: A sister tweet reinforcement process for modeling hashtag popularity, INFOCOM 2017
Supervisor: Niloy Ganguly
Projects:
Hashtag Popularity Prediction
In this project we aim to estimate the popularity of a hashtag in the future given its previous popularity
Publications:
CRPP: Competing Recurrent Point Process for Modeling Visibility Dynamics in Information Diffusion
Code and Data: https://github.com/ASCARATHIRA/CRPP