Exploring the dynamics of protest against National Register of Citizens & Citizenship Amendment Act through online social media: the Indian experience

• URL: http://arxiv.org/abs/2102.10531v1
• Date: Sun, 21 Feb 2021 06:56:20 GMT
• Title: Exploring the dynamics of protest against National Register of Citizens & Citizenship Amendment Act through online social media: the Indian experience
• Authors: Souvik Roy and Milan Mukherjee and Priyadarsini Sinha and Sukanta Das and Subhasis Bandopadhyay and Abhik Mukherjee
• Abstract summary: The study has put efforts to understand such dynamics in the context of the ongoing nationwide movement in India opposing the NRC-CAA enactment. The transformative nature of individual discontent into collective mobilization is presented here with a combination of qualitative (fieldwork) and quantitative (computing) techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The generic fluidity observed in the nature of political protest movements across the world during the last decade weigh heavily with the presence of social media. As such, there is a possibility to study the contemporary movements with an interdisciplinary approach combining computational analytics with social science perspectives. The present study has put efforts to understand such dynamics in the context of the ongoing nationwide movement in India opposing the NRC-CAA enactment. The transformative nature of individual discontent into collective mobilization, especially with a reflective intervention in social media across a sensitive region of the nation state, is presented here with a combination of qualitative (fieldwork) and quantitative (computing) techniques. The study is augmented further by the primary data generation coupled with real-time application of analytical approaches.

Related papers

• Community Shaping in the Digital Age: A Temporal Fusion Framework for Analyzing Discourse Fragmentation in Online Social Networks [45.58331196717468]
  This research presents a framework for analyzing the dynamics of online communities in social media platforms. By combining text classification and dynamic social network analysis, we uncover mechanisms driving community formation and evolution.
  arXiv  Detail & Related papers  (2024-09-18T03:03:02Z)
• Brief state of the art in social information mining: Practical application in analysis of trends in French legislative 2024 [0.0]
  This paper provides an overview of the state-of-the-art techniques in social media mining, with a practical application in analyzing trends in the 2024 French legislative elections. We leverage natural language processing (NLP) tools to gauge public opinion by extracting and analyzing comments and reactions from the AgoraVox platform. The study reveals that the National Rally party, led by Marine Le Pen, maintains a high level of engagement on social media, outperforming traditional parties.
  arXiv  Detail & Related papers  (2024-07-11T18:22:58Z)
• Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
  This comprehensive survey delves into the recent strides in HS moderation. We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
  arXiv  Detail & Related papers  (2024-01-30T03:51:44Z)
• Social media polarization reflects shifting political alliances in Pakistan [44.99833362998488]
  Spanning from 2018 to 2022, our analysis of Twitter data allows us to capture pivotal shifts and developments in Pakistan's political arena. By examining interactions and content generated by politicians affiliated with major political parties, we reveal a consistent and active presence of politicians on Twitter. Our analysis also uncovers significant shifts in political affiliations, including the transition of politicians to the opposition alliance.
  arXiv  Detail & Related papers  (2023-09-15T00:07:48Z)
• Expanding the Role of Affective Phenomena in Multimodal Interaction Research [57.069159905961214]
  We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing. We identify 910 affect-related papers and present our analysis of the role of affective phenomena in these papers. We find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states.
  arXiv  Detail & Related papers  (2023-05-18T09:08:39Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Didn't see that coming: a survey on non-verbal social human behavior forecasting [47.99589136455976]
  Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years. Its direct applications to human-robot interaction and socially-aware human motion generation make it a very attractive field. We define the behavior forecasting problem for multiple interactive agents in a generic way that aims at unifying the fields of social signals prediction and human motion forecasting.
  arXiv  Detail & Related papers  (2022-03-04T18:25:30Z)
• Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions [6.531659195805749]
  Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts. We investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media.
  arXiv  Detail & Related papers  (2021-09-01T16:28:24Z)
• Stance Detection on Social Media: State of the Art and Trends [5.584060970507506]
  Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied.
  arXiv  Detail & Related papers  (2020-06-05T19:24:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.