Dr. Sarabjeet Kaur Kochhar and Chinmay Chahar
Adv. Artif. Intell. Mach. Learn., 3 (4):1619-1639
Dr. Sarabjeet Kaur Kochhar : Indraprastha College for Women University of Delhi
Chinmay Chahar : Indira Gandhi Delhi Technical University for Women Department of Information Technology, Delhi, India
DOI: https://dx.doi.org/10.54364/AAIML.2023.1192
Article History: Received on: 27-Feb-23, Accepted on: 04-Nov-23, Published on: 11-Nov-23
Corresponding Author: Dr. Sarabjeet Kaur Kochhar
Email: skaur@ip.du.ac.in
Citation: Dr. Sarabjeet Kaur Kochhar, Chinmay Chahar (2023). Performing Stance Classification and Bot Detection on the Indian Farmers’ Protest – A Study to Unveil Hidden Perspectives.. Adv. Artif. Intell. Mach. Learn., 3 (4 ):1619-1639
The presence of
illegal, harmful content, rumors, misinformation, and Twitter bots has
consistently brought the social media platforms such as Twitter into the
spotlight. Therefore, it is advisable to exercise caution when analyzing
tweets. To establish the credibility of any patterns and findings derived from
tweets, it is essential to thoroughly investigate the source and authenticity
of the tweets in question. This paper advances in this direction by introducing
a novel approach involving bot detection and a comparative analysis of human
and bot-generated tweets related to the farmers' protest. A framework for
knowledge differentiation is deployed to accomplish this goal. The framework
unearths the global perspectives of people about Indian farmers’ protests, in the form
of stances, the results of which serve as nuggets of knowledge derived at the
lower level of abstraction. Unexpected results of stance detection motivated
the study of bot detection in each tweet of each stance. Knowledge discovered
by bot detection and characterization studies was thus built over stance
detection and yielded higher-order knowledge nuggets, which identified the widespread
presence of bots in tweets both for and against the protest, thus establishing
the misuse of social media platforms like Twitter to influence and control the
narrative of the social events that significantly impact people’s lives.
Characterization of issues being tweeted by humans vs. bots in favor of and
against farmers’ protests was accomplished by conducting a comparative
analysis of N-grams in each category. Vocabulary analysis established that
texts tweeted by bots mimicked the vocabulary pattern of the tweets by human
users. Research inferences such as these can be invaluable for policy makers,
enabling them to gain a macro-level understanding of the situations on the
ground level and leverage such information for making policy decisions, in
order to be prepared to handle similar situations in the future.