24 citations · 29 across the 3 of their papers we have counts for
6 papers
Boosting Entity Mention Detection for Targetted Twitter Streams with Global Contextual Embeddings
Satadisha Saha Bhowmick, Eduard C. Dragut, Weiyi Meng
Microblogging sites, like Twitter, have emerged as ubiquitous sources of information. Two important tasks related to the automatic extraction and analysis of information in Microbl…
Cannot Predict Comment Volume of a News Article before (a few) Users Read It
Lihong He, Chen Shen, Arjun Mukherjee +2
Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, i.e., commen…
Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation
Yigeng Zhang, Fan Yang, Yifan Zhang +2
Satirical news is regularly shared in modern social media because it is entertaining with smartly embedded humor. However, it can be harmful to society because it can sometimes be…
Stance Prediction for Contemporary Issues: Data and Experiments
Marjan Hosseinia, Eduard Dragut, Arjun Mukherjee
We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part…
An Accountable Anonymous Data Aggregation Scheme for Internet of Things
Longfei Wu, Xiaojiang Du, Jie Wu +2
The Internet of Things (IoT) has become increasingly popular in people's daily lives. The pervasive IoT devices are encouraged to share data with each other in order to better serv…
Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features
Fan Yang, Arjun Mukherjee, Eduard Dragut
Satirical news is considered to be entertainment, but it is potentially deceptive and harmful. Despite the embedded genre in the article, not everyone can recognize the satirical c…