Fred Morstatter

7.3k total citations · 2 hit papers
80 papers, 3.3k citations indexed

About

Fred Morstatter is a scholar working on Artificial Intelligence, Sociology and Political Science and Statistical and Nonlinear Physics. According to data from OpenAlex, Fred Morstatter has authored 80 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Artificial Intelligence, 28 papers in Sociology and Political Science and 25 papers in Statistical and Nonlinear Physics. Recurrent topics in Fred Morstatter's work include Misinformation and Its Impacts (22 papers), Complex Network Analysis Techniques (22 papers) and Spam and Phishing Detection (17 papers). Fred Morstatter is often cited by papers focused on Misinformation and Its Impacts (22 papers), Complex Network Analysis Techniques (22 papers) and Spam and Phishing Detection (17 papers). Fred Morstatter collaborates with scholars based in United States, Germany and Austria. Fred Morstatter's co-authors include Huan Liu, Huan Liu, Jiliang Tang, Suhang Wang, Kewei Cheng, Jundong Li, Robert P. Treviño, Liang Wu, Kathleen M. Carley and Shamanth Kumar and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Scientific Reports.

In The Last Decade

Fred Morstatter

73 papers receiving 3.2k citations

Hit Papers

align trajectories

What are hit papers?

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.

Feature Selection

2017 1.5k citations Jundong Li, Kewei Cheng et al. ACM Computing Surveys profile →
Misinformation in Social Media

2019 224 citations Liang Wu, Fred Morstatter et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Fred Morstatter United States	24	1.5k	894	703	483	420	80	3.3k
Aron Culotta United States	30	2.0k 1.3×	696 0.8×	578 0.8×	206 0.4×	351 0.8×	72	3.3k
John Yen United States	34	2.0k 1.3×	444 0.5×	702 1.0×	337 0.7×	361 0.9×	184	4.8k
Jie Yin Australia	28	1.7k 1.1×	403 0.5×	511 0.7×	603 1.2×	639 1.5×	98	3.6k
Derek Greene Ireland	20	1.1k 0.7×	382 0.4×	515 0.7×	364 0.8×	531 1.3×	94	2.3k
Symeon Papadopoulos Greece	27	1.3k 0.8×	704 0.8×	669 1.0×	800 1.7×	725 1.7×	172	2.8k
Hanna Wallach United States	26	3.0k 2.0×	578 0.6×	748 1.1×	411 0.9×	351 0.8×	67	4.9k
Diyi Yang United States	35	4.7k 3.1×	837 0.9×	1.1k 1.5×	692 1.4×	228 0.5×	169	7.0k
Yu‐Ru Lin United States	27	1.0k 0.7×	532 0.6×	516 0.7×	744 1.5×	1.2k 2.8×	177	3.2k
Nigel Shadbolt United Kingdom	37	2.6k 1.7×	989 1.1×	1.9k 2.7×	430 0.9×	280 0.7×	270	5.6k
Junjie Wu China	37	2.6k 1.7×	483 0.5×	912 1.3×	805 1.7×	588 1.4×	191	6.1k

Countries citing papers authored by Fred Morstatter

Since Specialization

Citations

This map shows the geographic impact of Fred Morstatter'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 Fred Morstatter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fred Morstatter more than expected).

Fields of papers citing papers by Fred Morstatter

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fred Morstatter. 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 Fred Morstatter. The network helps show where Fred Morstatter may publish in the future.

Co-authorship network of co-authors of Fred Morstatter

This figure shows the co-authorship network connecting the top 25 collaborators of Fred Morstatter. A scholar is included among the top collaborators of Fred Morstatter 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 Fred Morstatter. Fred Morstatter is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Morstatter, Fred, et al.. (2025). Characterizing Network Structure of Anti-Trans Actors on TikTok. 472–483.

2.

Sethi, Rajiv, et al.. (2024). Evaluating Prediction Mechanisms: A Profitability Test. 29–40.

3.

Morstatter, Fred, et al.. (2024). Artificial Intuition: Efficient Classification of Scientific Abstracts. 191–201. 1 indexed citations

4.

Hopp, Frederic R., et al.. (2024). Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks. UvA-DARE (University of Amsterdam). 7337–7349. 1 indexed citations

5.

Morstatter, Fred, et al.. (2024). The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance. 4629–4651. 9 indexed citations

6.

Pfeffer, Jürgen, Daniel Matter, Kokil Jaidka, et al.. (2023). Just Another Day on Twitter: A Complete 24 Hours of Twitter Data. Proceedings of the International AAAI Conference on Web and Social Media. 17. 1073–1081. 14 indexed citations

7.

Morstatter, Fred, et al.. (2023). Pipeline for modeling causal beliefs from natural language. 436–443. 1 indexed citations

8.

Shaikh, Omar, et al.. (2023). Modeling Cross-Cultural Pragmatic Inference with Codenames Duet. 6550–6569. 4 indexed citations

9.

Mehrabi, Ninareh, Pei Zhou, Fred Morstatter, et al.. (2021). Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 5016–5033. 19 indexed citations

10.

Chakrabarty, Tuhin, et al.. (2021). Identifying Distributional Perspectives from Colingual Groups. 178–190. 2 indexed citations

11.

Jin, Woojeong, Rahul Khanna, Dong‐Ho Lee, et al.. (2020). ForecastQA: A Question Answering Challenge for Event Forecasting. arXiv (Cornell University). 1 indexed citations

12.

Pfeffer, Jürgen, Katja Mayer, & Fred Morstatter. (2018). Tampering with Twitter’s Sample API. EPJ Data Science. 7(1). 69 indexed citations

13.

Morstatter, Fred & Huan Liu. (2018). In Search of Coherence and Consensus: Measuring the Interpretability of Statistical Topics. Journal of Machine Learning Research. 18(169). 1–32. 22 indexed citations

14.

Wu, Liang, Fred Morstatter, & Huan Liu. (2018). SlangSD: building, expanding and using a sentiment dictionary of slang words for short-text sentiment classification. Language Resources and Evaluation. 52(3). 839–852. 42 indexed citations

15.

Li, Jundong, Kewei Cheng, Suhang Wang, et al.. (2017). Feature Selection. ACM Computing Surveys. 50(6). 1–45. 1541 indexed citations breakdown →

16.

Morstatter, Fred, Liang Wu, Tahora H. Nazer, Kathleen M. Carley, & Huan Liu. (2016). A new approach to bot detection: striking the balance between precision and recall. 533–540. 71 indexed citations

17.

Morstatter, Fred, et al.. (2016). Can One Tamper with the Sample API?. 81–82. 16 indexed citations

18.

Morstatter, Fred, Huiji Gao, & Huan Liu. (2015). Discovering Location Information in Social Media.. IEEE Data(base) Engineering Bulletin. 38. 4–13. 9 indexed citations

19.

Morstatter, Fred, Shamanth Kumar, Huan Liu, & Ross Maciejewski. (2013). Understanding Twitter data with TweetXplorer. 1482–1485. 53 indexed citations

20.

Kumar, Shamanth, Fred Morstatter, Reza Zafarani, & Huan Liu. (2013). Whom should I follow?. 139–147. 42 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.

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