David Muchlinski

692 total citations
10 papers, 382 citations indexed

About

David Muchlinski is a scholar working on Sociology and Political Science, Political Science and International Relations and Artificial Intelligence. According to data from OpenAlex, David Muchlinski has authored 10 papers receiving a total of 382 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Sociology and Political Science, 5 papers in Political Science and International Relations and 2 papers in Artificial Intelligence. Recurrent topics in David Muchlinski's work include Political Conflict and Governance (6 papers), Electoral Systems and Political Participation (2 papers) and Global Security and Public Health (1 paper). David Muchlinski is often cited by papers focused on Political Conflict and Governance (6 papers), Electoral Systems and Political Participation (2 papers) and Global Security and Public Health (1 paper). David Muchlinski collaborates with scholars based in United States, United Kingdom and Australia. David Muchlinski's co-authors include Jingrui He, Matthew Adam Kocher, David S. Siroky, Sarah Birch, Munmun De Choudhury, Caleb Ziems, Diyi Yang, Mai ElSherief, Charles Butcher and Benjamin E. Goldsmith and has published in prestigious journals such as American Political Science Review, Journal of Conflict Resolution and Political Analysis.

In The Last Decade

David Muchlinski

9 papers receiving 359 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David Muchlinski United States	7	181	114	80	21	19	10	382
Michael Haman Czechia	7	85 0.5×	59 0.5×	36 0.5×	60 2.9×	10 0.5×	25	250
Cassy Dorff United States	11	247 1.4×	24 0.2×	92 1.1×	14 0.7×	7 0.4×	21	352
Mark Olsen United States	9	116 0.6×	68 0.6×	16 0.2×	6 0.3×	16 0.8×	40	378
Adina Nerghes Netherlands	9	107 0.6×	52 0.5×	23 0.3×	65 3.1×	11 0.6×	14	245
Micha Germann Switzerland	12	165 0.9×	114 1.0×	241 3.0×	144 6.9×	16 0.8×	22	383
Sahana Udupa Germany	12	172 1.0×	237 2.1×	86 1.1×	149 7.1×	25 1.3×	42	520
Raúl Sierra United States	8	177 1.0×	20 0.2×	81 1.0×	9 0.4×	17 0.9×	16	372
Jason Pilny Radford United States	5	138 0.8×	43 0.4×	15 0.2×	49 2.3×	20 1.1×	10	287
Matthias Leese Switzerland	13	368 2.0×	42 0.4×	196 2.5×	19 0.9×	72 3.8×	33	595

Countries citing papers authored by David Muchlinski

Since Specialization

Citations

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

Fields of papers citing papers by David Muchlinski

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Muchlinski

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

All Works

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10 of 10 papers shown

Ziems, Caleb, et al.. (2024). Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles. 12493–12509.

ElSherief, Mai, et al.. (2021). Latent Hatred: A Benchmark for Understanding Implicit Hate Speech. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 345–363. 89 indexed citations

Muchlinski, David. (2021). Swords and Plowshares: Property Rights, Collective Action, and Nonstate Governance in the Jewish Community of Palestine 1920–1948. American Political Science Review. 115(4). 1373–1387. 4 indexed citations

Muchlinski, David, et al.. (2020). We need to go deeper: measuring electoral violence using convolutional neural networks and social media. Political Science Research and Methods. 9(1). 122–139. 13 indexed citations

Butcher, Charles, et al.. (2020). Introducing the Targeted Mass Killing Data Set for the Study and Forecasting of Mass Atrocities. Journal of Conflict Resolution. 64(7-8). 1524–1547. 24 indexed citations

Muchlinski, David, David S. Siroky, Jingrui He, & Matthew Adam Kocher. (2018). Seeing the Forest through the Trees. Political Analysis. 27(1). 111–113. 3 indexed citations

Birch, Sarah & David Muchlinski. (2017). The Dataset of Countries at Risk of Electoral Violence. Terrorism and Political Violence. 32(2). 217–236. 35 indexed citations

Birch, Sarah & David Muchlinski. (2017). Electoral violence prevention: what works?. Democratization. 25(3). 385–403. 33 indexed citations

Muchlinski, David, David S. Siroky, Jingrui He, & Matthew Adam Kocher. (2015). Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data. Political Analysis. 24(1). 87–103. 162 indexed citations

10.

Muchlinski, David. (2014). Grievances and Opportunities: Religious Violence across Political Regimes. Politics and Religion. 7(4). 684–705. 19 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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