Richard Sear

434 total citations
11 papers, 186 citations indexed

About

Richard Sear is a scholar working on Sociology and Political Science, Artificial Intelligence and Communication. According to data from OpenAlex, Richard Sear has authored 11 papers receiving a total of 186 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Sociology and Political Science, 7 papers in Artificial Intelligence and 6 papers in Communication. Recurrent topics in Richard Sear's work include Hate Speech and Cyberbullying Detection (7 papers), Social Media and Politics (6 papers) and Misinformation and Its Impacts (6 papers). Richard Sear is often cited by papers focused on Hate Speech and Cyberbullying Detection (7 papers), Social Media and Politics (6 papers) and Misinformation and Its Impacts (6 papers). Richard Sear collaborates with scholars based in United States, Finland and Bulgaria. Richard Sear's co-authors include Neil F. Johnson, Nicholas J. Restrepo, Yonatan Lupu, Rhys Leahy, Nicolás Velásquez, Nicholas Gabriel, Sara El Oud, Beth Goldberg, Pedro D. Manrique and Minzhang Zheng and has published in prestigious journals such as PLoS ONE, Scientific Reports and IEEE Access.

In The Last Decade

Richard Sear

10 papers receiving 174 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Richard Sear United States	5	113	89	44	28	24	11	186
Ramya Tekumalla United States	5	113 1.0×	102 1.1×	23 0.5×	15 0.5×	27 1.1×	7	193
Yuning Ding Germany	5	109 1.0×	128 1.4×	19 0.4×	14 0.5×	36 1.5×	10	212
Ekaterina Artemova Russia	6	113 1.0×	170 1.9×	19 0.4×	15 0.5×	28 1.2×	16	258
Xiaolei Huang United States	10	57 0.5×	135 1.5×	11 0.3×	13 0.5×	27 1.1×	24	275
Zach Wood-Doughty United States	8	68 0.6×	164 1.8×	22 0.5×	3 0.1×	27 1.1×	11	283
Sanmitra Bhattacharya United States	8	114 1.0×	92 1.0×	67 1.5×	3 0.1×	25 1.0×	20	252
Klaifer Garcia Brazil	3	123 1.1×	151 1.7×	38 0.9×	5 0.2×	33 1.4×	4	232
Pavan Holur United States	4	176 1.6×	59 0.7×	38 0.9×	4 0.1×	30 1.3×	9	209
Jing Su China	8	100 0.9×	65 0.7×	27 0.6×	4 0.1×	27 1.1×	30	206
Thayer Alshaabi United States	9	74 0.7×	39 0.4×	45 1.0×	10 0.4×	3 0.1×	18	178

Countries citing papers authored by Richard Sear

Since Specialization

Citations

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

Fields of papers citing papers by Richard Sear

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Richard Sear

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

All Works

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11 of 11 papers shown

1.

Sear, Richard, et al.. (2024). How U.S. Presidential elections strengthen global hate networks. PubMed. 1(1). 18–18.

2.

Zheng, Minzhang, et al.. (2024). Adaptive link dynamics drive online hate networks and their mainstream influence. 1(1). 2 indexed citations

3.

Johnson, Neil F., et al.. (2023). Controlling bad-actor-artificial intelligence activity at scale across online battlefields. PNAS Nexus. 3(1). pgae004–pgae004. 3 indexed citations

4.

Lupu, Yonatan, Richard Sear, Nicolás Velásquez, et al.. (2023). Offline events and online hate. PLoS ONE. 18(1). e0278511–e0278511. 25 indexed citations

5.

Leahy, Rhys, Nicholas J. Restrepo, Richard Sear, & Neil F. Johnson. (2022). Connectivity Between Russian Information Sources and Extremist Communities Across Social Media Platforms. Frontiers in Political Science. 4. 4 indexed citations

6.

Sear, Richard, Nicholas J. Restrepo, Yonatan Lupu, & Neil Johnson. (2022). Dynamic Latent Dirichlet Allocation Tracks Evolution of Online Hate Topics. 2(1). 257–272. 1 indexed citations

7.

Restrepo, Nicholas J., et al.. (2021). How Social Media Machinery Pulled Mainstream Parenting Communities Closer to Extremes and Their Misinformation During Covid-19. IEEE Access. 10. 2330–2344. 13 indexed citations

8.

Velásquez, Nicolás, Pedro D. Manrique, Richard Sear, et al.. (2021). Hidden order across online extremist movements can be disrupted by nudging collective chemistry. Scientific Reports. 11(1). 9965–9965. 4 indexed citations

9.

Velásquez, Nicolás, Rhys Leahy, Nicholas J. Restrepo, et al.. (2021). Online hate network spreads malicious COVID-19 content outside the control of individual social media platforms. Scientific Reports. 11(1). 11549–11549. 44 indexed citations

10.

Sear, Richard, Nicholas J. Restrepo, Yonatan Lupu, & Neil F. Johnson. (2021). Machine Learning Language Models: Achilles Heel for Social Media Platforms and a Possible Solution. 1(3). 191–202. 6 indexed citations

11.

Sear, Richard, Nicolás Velásquez, Rhys Leahy, et al.. (2020). Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning. IEEE Access. 8. 91886–91893. 84 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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