Matthew Costello

816 total citations
28 papers, 486 citations indexed

About

Matthew Costello is a scholar working on Artificial Intelligence, Sociology and Political Science and Communication. According to data from OpenAlex, Matthew Costello has authored 28 papers receiving a total of 486 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 19 papers in Sociology and Political Science and 15 papers in Communication. Recurrent topics in Matthew Costello's work include Hate Speech and Cyberbullying Detection (21 papers), Social Media and Politics (15 papers) and Social and Intergroup Psychology (7 papers). Matthew Costello is often cited by papers focused on Hate Speech and Cyberbullying Detection (21 papers), Social Media and Politics (15 papers) and Social and Intergroup Psychology (7 papers). Matthew Costello collaborates with scholars based in United States, Spain and Finland. Matthew Costello's co-authors include James E.Hawdon, Amanda Brown Cross, Feng Luo, Nishant Vishwamitra, Ashley V. Reichelmann, Cheng Long, Vicente J. Llorent, Pekka Räsänen, Catherine Blaya and Atte Oksanen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Computers in Human Behavior and Social Forces.

In The Last Decade

Matthew Costello

26 papers receiving 468 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthew Costello United States 13 298 294 223 161 76 28 486
Jordana N. Navarro United States 10 122 0.4× 261 0.9× 27 0.1× 154 1.0× 108 1.4× 23 370
Phyllis B. Gerstenfeld United States 6 142 0.5× 236 0.8× 119 0.5× 67 0.4× 32 0.4× 8 383
Jana Laura Egelhofer Austria 7 208 0.7× 455 1.5× 357 1.6× 18 0.1× 52 0.7× 10 559
Colette Langos Australia 4 115 0.4× 104 0.4× 38 0.2× 234 1.5× 20 0.3× 14 284
Jin Ree Lee United States 11 72 0.2× 211 0.7× 20 0.1× 123 0.8× 133 1.8× 14 341
Diana R. Grant United States 4 110 0.4× 166 0.6× 111 0.5× 35 0.2× 29 0.4× 6 277
Ashley V. Reichelmann United States 9 71 0.2× 138 0.5× 51 0.2× 55 0.3× 13 0.2× 18 220
Daniel Delmonaco United States 7 134 0.4× 131 0.4× 109 0.5× 30 0.2× 13 0.2× 12 267
Chandell Gosse Canada 7 71 0.2× 133 0.5× 74 0.3× 28 0.2× 18 0.2× 18 252
Tony Wang United States 4 137 0.5× 108 0.4× 115 0.5× 28 0.2× 36 0.5× 8 254

Countries citing papers authored by Matthew Costello

Since Specialization
Citations

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

Fields of papers citing papers by Matthew Costello

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Costello

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

All Works

20 of 20 papers shown
1.
Cheng, Long, et al.. (2023). Analysis of COVID-19 Offensive Tweets and Their Targets. 4473–4484. 1 indexed citations
2.
E.Hawdon, James, Ashley V. Reichelmann, Matthew Costello, et al.. (2023). Measuring Hate: Does a Definition Affect Self-Reported Levels of Perpetration and Exposure to Online Hate in Surveys?. Social Science Computer Review. 42(3). 812–831. 10 indexed citations
3.
Costello, Matthew, et al.. (2023). COVID-19 and Sinophobia: Detecting Warning Signs of Radicalization on Twitter and Reddit. Cyberpsychology Behavior and Social Networking. 26(7). 546–553.
4.
Costello, Matthew, James E.Hawdon, Ashley V. Reichelmann, et al.. (2023). Defending Others Online: The Influence of Observing Formal and Informal Social Control on One’s Willingness to Defend Cyberhate Victims. International Journal of Environmental Research and Public Health. 20(15). 6506–6506. 2 indexed citations
5.
Costello, Matthew, Ashley V. Reichelmann, & James E.Hawdon. (2022). Utilizing criminological theories to predict involvement in cyberviolence among the iGeneration. Sociological Spectrum. 42(4-6). 260–277. 2 indexed citations
6.
Celuch, Magdalena, Atte Oksanen, Pekka Räsänen, et al.. (2022). Factors Associated with Online Hate Acceptance: A Cross-National Six-Country Study among Young Adults. International Journal of Environmental Research and Public Health. 19(1). 534–534. 23 indexed citations
7.
Costello, Matthew, et al.. (2021). Who Produces Online Hate?: An Examination of the Effects of Self-Control, Social Structure, & Social Learning. American Journal of Criminal Justice. 47(3). 421–440. 12 indexed citations
8.
Reichelmann, Ashley V. & Matthew Costello. (2021). When Patriot Becomes Hate-triot: The Relationship Between American Identity and the Production of Cyberhate. American Journal of Criminal Justice. 46(6). 956–979.
9.
10.
Li, Mingqi, et al.. (2021). COVID-HateBERT: a Pre-trained Language Model for COVID-19 related Hate Speech Detection. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). 233–238. 15 indexed citations
11.
Wachs, Sebastian, Matthew Costello, Michelle F. Wright, et al.. (2020). “DNT LET ’EM H8 U!”: Applying the routine activity framework to understand cyberhate victimization among adolescents across eight countries. Computers & Education. 160. 104026–104026. 42 indexed citations
12.
Vishwamitra, Nishant, et al.. (2020). On the Impact of Word Representation in Hate Speech and Offensive Language Detection and Explanation. 171–173. 4 indexed citations
13.
Vishwamitra, Nishant, et al.. (2020). On Analyzing COVID-19-related Hate Speech Using BERT Attention. 669–676. 18 indexed citations
14.
Costello, Matthew, et al.. (2019). Social Group Identity and Perceptions of Online Hate*. Sociological Inquiry. 89(3). 427–452. 37 indexed citations
15.
E.Hawdon, James, et al.. (2019). The Perpetuation of Online Hate: A Criminological Analysis of Factors Associated with Participating in an Online Attack. SHILAP Revista de lepidopterología. 15(1). 10 indexed citations
16.
Costello, Matthew, et al.. (2018). We don’t like your type around here: Regional and residential differences in exposure to online hate material targeting sexuality. Deviant Behavior. 40(3). 385–401. 17 indexed citations
17.
E.Hawdon, James, et al.. (2018). Cyber-Routines, Political Attitudes, and Exposure to Violence-Advocating Online Extremism. Social Forces. 98(1). 329–354. 31 indexed citations
18.
Costello, Matthew, James E.Hawdon, & Amanda Brown Cross. (2016). Virtually Standing Up or Standing By? Correlates of Enacting Social Control Online. International Journal of Criminology and Sociology. 6. 16–28. 9 indexed citations
19.
Costello, Matthew, et al.. (2016). Who views online extremism? Individual attributes leading to exposure. Computers in Human Behavior. 63. 311–320. 93 indexed citations
20.
Costello, Matthew & Erik D. Reichle. (2004). LSDNet: A Neural Network for Multisensory Perception.. 341–341. 3 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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