A Weakly Supervised Approach for Classifying Stance in Twitter Replies

by   Sumeet Kumar, et al.

Conversations on social media (SM) are increasingly being used to investigate social issues on the web, such as online harassment and rumor spread. For such issues, a common thread of research uses adversarial reactions, e.g., replies pointing out factual inaccuracies in rumors. Though adversarial reactions are prevalent in online conversations, inferring those adverse views (or stance) from the text in replies is difficult and requires complex natural language processing (NLP) models. Moreover, conventional NLP models for stance mining need labeled data for supervised learning. Getting labeled conversations can itself be challenging as conversations can be on any topic, and topics change over time. These challenges make learning the stance a difficult NLP problem. In this research, we first create a new stance dataset comprised of three different topics by labeling both users' opinions on the topics (as in pro/con) and users' stance while replying to others' posts (as in favor/oppose). As we find limitations with supervised approaches, we propose a weakly-supervised approach to predict the stance in Twitter replies. Our novel method allows using a smaller number of hashtags to generate weak labels for Twitter replies. Compared to supervised learning, our method improves the mean F1-macro by 8% on the hand-labeled dataset without using any hand-labeled examples in the training set. We further show the applicability of our proposed method on COVID 19 related conversations on Twitter.



There are no comments yet.


page 1

page 11


Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance in Twitter Conversations

Automated ways to extract stance (denying vs. supporting opinions) from ...

Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions

Cannabis legalization has been welcomed by many U.S. states but its role...

Data-Driven Dialogue Systems for Social Agents

In order to build dialogue systems to tackle the ambitious task of holdi...

Weakly Supervised Medication Regimen Extraction from Medical Conversations

Automated Medication Regimen (MR) extraction from medical conversations ...

Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains

Social media has become a valuable resource for the study of suicidal id...

VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination

Nowadays, Social network sites (SNSs) such as Facebook, Twitter are comm...

Fine-grained Geolocation Prediction of Tweets with Human Machine Collaboration

Twitter is a useful resource to analyze peoples' opinions on various top...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.