Drift in Online Social Media

Mullick, Ankan; Bhandari, Anindya; Niranjan, Abhishek; Sckhar, Nitesh; Garg, Shrey; Bubna, Riya; Roy, Mayank

doi:10.1109/iemcon.2018.8614746

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Chakraborty et al [22,23] utilized these different popularity signals for online news recommendations. Mullick et al [24] experimented topic drift event and characteristics in online comments.…”

Section: Related Workmentioning

confidence: 99%

Public Sphere 2.0: Targeted Commenting in Online News Media

Mullick

Ghosh

Dutt

et al. 2019

Lecture Notes in Computer Science

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With the increase in online news consumption, to maximize advertisement revenue, news media websites try to attract and retain their readers on their sites. One of the most effective tools for reader engagement is commenting, where news readers post their views as comments against the news articles. Traditionally, it has been assumed that the comments are mostly made against the full article. In this work, we show that present commenting landscape is far from this assumption. Because the readers lack the time to go over an entire article, most of the comments are relevant to only particular sections of an article. In this paper, we build a system which can automatically classify comments against relevant sections of an article. To implement that, we develop a deep neural network based mechanism to find comments relevant to any section and a paragraph wise commenting interface to showcase them. We believe that such a data driven commenting system can help news websites to further increase reader engagement.

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Section: Related Workmentioning

confidence: 99%

Public Sphere 2.0: Targeted Commenting in Online News Media

Mullick

Ghosh

Dutt

et al. 2019

Lecture Notes in Computer Science

Self Cite

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On Label Quality in Class Imbalance Setting -A Case Study

Alshehri

Stanojević

Dragut

et al. 2022

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

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Infodemic: Challenges and solutions in topic discovery and data process

Zhang,

Pan,

Lin

et al. 2023

Arch Public Health

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Background The Coronavirus Disease 2019 (COVID-19) pandemic was a huge shock to society, and the ensuing information problems had a huge impact on society at the same time. The urgent need to understand the Infodemic, i.e., the importance of the spread of false information related to the epidemic, has been highlighted. However, while there is a growing interest in this phenomenon, studies on the topic discovery, data collection, and data preparation phases of the information analysis process have been lacking. Objective Since the epidemic is unprecedented and has not ended to this day, we aimed to examine the existing Infodemic-related literature from January 2019 to December 2022. Methods We have systematically searched ScienceDirect and IEEE Xplore databases with some search limitations. From the searched literature we selected titles, abstracts and keywords, and limitations sections. We conducted an extensive structured literature search and analysis by filtering the literature and sorting out the available information. Results A total of 47 papers ended up meeting the requirements of this review. Researchers in all of these literatures encountered different challenges, most of which were focused on the data collection step, with few challenges encountered in the data preparation phase and almost none in the topic discovery section. The challenges were mainly divided into the points of how to collect data quickly, how to get the required data samples, how to filter the data, what to do if the data set is too small, how to pick the right classifier and how to deal with topic drift and diversity. In addition, researchers have proposed partial solutions to the challenges, and we have also proposed possible solutions. Conclusions This review found that Infodemic is a rapidly growing research area that attracts the interest of researchers from different disciplines. The number of studies in this field has increased significantly in recent years, with researchers from different countries, including the United States, India, and China. Infodemic topic discovery, data collection, and data preparation are not easy, and each step faces different challenges. While there is some research in this emerging field, there are still many challenges that need to be addressed. These findings highlight the need for more articles to address these issues and fill these gaps.

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Drift in Online Social Media

Cited by 4 publications

References 16 publications

Public Sphere 2.0: Targeted Commenting in Online News Media

Public Sphere 2.0: Targeted Commenting in Online News Media

On Label Quality in Class Imbalance Setting -A Case Study

Infodemic: Challenges and solutions in topic discovery and data process

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