no code implementations • 10 Sep 2019 • Binny Mathew, Suman Kalyan Maity, Pawan Goyal, Animesh Mukherjee
Our system is also able to predict ~ 25% of the correct case of merges within the first month of the merge and ~ 40% of the cases within a year.
1 code implementation • 10 Mar 2019 • Suman Kalyan Maity, Abhishek Panigrahi, Sayan Ghosh, Arundhati Banerjee, Pawan Goyal, Animesh Mukherjee
In this paper, we develop a content-cum-user based deep learning framework DeepTagRec to recommend appropriate question tags on Stack Overflow.
no code implementations • 17 Nov 2018 • Binny Mathew, Ritam Dutt, Suman Kalyan Maity, Pawan Goyal, Animesh Mukherjee
In particular, we observe that the choice to post the question as anonymous is dependent on the user's perception of anonymity and they often choose to speak about depression, anxiety, social ties and personal issues under the guise of anonymity.
2 code implementations • Proceedings of the International AAAI Conference on Web and Social Media 2019 • Binny Mathew, Hardik Tharad, Subham Rajgaria, Prajwal Singhania, Suman Kalyan Maity, Pawan Goyal, Animesh Mukherje
In this paper, we create and release the first ever dataset for counterspeech using comments from YouTube.
Social and Information Networks
no code implementations • WS 2017 • Binny Mathew, Suman Kalyan Maity, Pratip Sarkar, Animesh Mukherjee, Pawan Goyal
Word senses are not static and may have temporal, spatial or corpus-specific scopes.
no code implementations • 11 Apr 2017 • K. C. Santosh, Suman Kalyan Maity, Arjun Mukherjee
We propose ENWalk, a framework to detect the spammers by learning the feature representations of the users in the social media.
no code implementations • 11 Mar 2017 • Suman Kalyan Maity, Aman Kharb, Animesh Mukherjee
Notably, features representing the language use patterns of the users are most discriminative and alone account for an accuracy of 74. 18%.
no code implementations • 31 Jan 2016 • Suman Kalyan Maity, Chaitanya Sarda, Anshit Chaudhary, Abhijeet Patil, Shraman Kumar, Akash Mondal, Animesh Mukherjee
Language in social media is mostly driven by new words and spellings that are constantly entering the lexicon thereby polluting it and resulting in high deviation from the formal written version.