Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Spotting fake reviewer groups in consumer reviews
2012488 citationsArjun Mukherjee, Bing Liu et al.profile →
Spotting opinion spammers using behavioral footprints
2013267 citationsArjun Mukherjee, Bing Liu et al.profile →
What Yelp Fake Review Filter Might Be Doing?
2021154 citationsArjun Mukherjee, Vivek V. Venkataraman et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Arjun Mukherjee
Since
Specialization
Citations
This map shows the geographic impact of Arjun Mukherjee's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Arjun Mukherjee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arjun Mukherjee more than expected).
This network shows the impact of papers produced by Arjun Mukherjee. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Arjun Mukherjee. The network helps show where Arjun Mukherjee may publish in the future.
Co-authorship network of co-authors of Arjun Mukherjee
This figure shows the co-authorship network connecting the top 25 collaborators of Arjun Mukherjee.
A scholar is included among the top collaborators of Arjun Mukherjee based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Arjun Mukherjee. Arjun Mukherjee is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhang, Yifan, et al.. (2018). Experiments with Convolutional Neural Networks for Multi-Label Authorship Attribution.. Language Resources and Evaluation.11 indexed citations
7.
Mukherjee, Arjun, et al.. (2018). A Parallel Hierarchical Attention Network for Style Change Detection: Notebook for PAN at CLEF 2018.. CLEF (Working Notes).1 indexed citations
8.
Yang, Fan, et al.. (2018). Attending Sentences to detect Satirical Fake News.. International Conference on Computational Linguistics. 3371–3380.33 indexed citations
9.
Yang, Fan, Arjun Mukherjee, & Yifan Zhang. (2016). Leveraging Multiple Domains for Sentiment Classification.. International Conference on Computational Linguistics. 2978–2988.2 indexed citations
Mukherjee, Arjun & Bing Liu. (2013). Discovering User Interactions in Ideological Discussions. Meeting of the Association for Computational Linguistics. 671–681.13 indexed citations
13.
Chen, Zhiyuan, Arjun Mukherjee, Bing Liu, et al.. (2013). Leveraging multi-domain prior knowledge in topic models. International Joint Conference on Artificial Intelligence. 2071–2077.57 indexed citations
14.
Mukherjee, Arjun, Vivek V. Venkataraman, Bing Liu, & Sharon Meraz. (2013). Public Dialogue: Analysis of Tolerance in Online Discussions. Meeting of the Association for Computational Linguistics. 1680–1690.12 indexed citations
Mukherjee, Arjun & Bing Liu. (2012). Modeling Review Comments. Meeting of the Association for Computational Linguistics. 320–329.36 indexed citations
17.
Mukherjee, Arjun & Bing Liu. (2012). Aspect Extraction through Semi-Supervised Modeling. Meeting of the Association for Computational Linguistics. 1. 339–348.216 indexed citations
18.
Mukherjee, Arjun & Bing Liu. (2012). Analysis of Linguistic Style Accommodation in Online Debates. International Conference on Computational Linguistics. 1831–1846.10 indexed citations
19.
Mukherjee, Arjun & Bing Liu. (2010). Improving Gender Classification of Blog Authors. Empirical Methods in Natural Language Processing. 207–217.128 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.