Characterizing Suspicious Commenter Behaviors-Reference-Cited by-同舟云学术

Characterizing Suspicious Commenter Behaviors

Published:2023-11-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the International Conference on Advances in Social Networks Analysis and Mining
language:
Short-container-title:

Author:

Shajari Shadi¹^ORCID,Alassad Mustafa¹^ORCID,Agarwal Nitin¹^ORCID

Affiliation:

1. COSMOS Research Center- University of Arkansas at Little Rock, Little Rock, United States

Funder

U.S. National Science Foundation

U.S. Air Force Research Laboratory, U.S. Army Research Office

U.S. Defense Advanced Research Projects Agency

Australian Department of Defense Strategic Policy Grants Program (SPGP)

U.S. Office of Naval Research

U.S. Office of the Under Secretary of Defense for Research and Engineering

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3625007.3627309

Reference22 articles.

1. A COMPARATIVE ANALYSIS OF K-MEANS AND HIERARCHICAL CLUSTERING

2. A qualitative–quantitative study of science mapping by different algorithms: The Polish journals landscape

3. A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants

4. J. H. Lau and T. Baldwin, "An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation." arXiv, Jul. 18, 2016. Accessed: May 04, 2023. [Online]. Available: http://arxiv.org/abs/1607.05368.

5. Analyzing Disinformation and Crowd Manipulation Tactics on YouTube

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The bias beneath: analyzing drift in YouTube’s algorithmic recommendations;Social Network Analysis and Mining;2024-08-24

2. The Multi-attribute impact of hyperlinks in blogs: an emotion-centric approach;Social Network Analysis and Mining;2024-07-12

3. Developing Epidemiological Models with Differentiated Infected Intensity;Lecture Notes in Computer Science;2024

4. Unveiling Bias in YouTube Shorts: Analyzing Thumbnail Recommendations and Topic Dynamics;Lecture Notes in Computer Science;2024