Vito D’Orazio

1.1k total citations · 1 hit paper
34 papers, 576 citations indexed

About

Vito D’Orazio is a scholar working on Artificial Intelligence, Sociology and Political Science and Political Science and International Relations. According to data from OpenAlex, Vito D’Orazio has authored 34 papers receiving a total of 576 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 10 papers in Sociology and Political Science and 6 papers in Political Science and International Relations. Recurrent topics in Vito D’Orazio's work include Natural Language Processing Techniques (10 papers), Topic Modeling (9 papers) and Political Conflict and Governance (5 papers). Vito D’Orazio is often cited by papers focused on Natural Language Processing Techniques (10 papers), Topic Modeling (9 papers) and Political Conflict and Governance (5 papers). Vito D’Orazio collaborates with scholars based in United States, Netherlands and Italy. Vito D’Orazio's co-authors include Glenn Palmer, Michael Kenwick, Matthew Lane, Idean Salehyan, Roseanne W. McManus, Patrick T. Brandt, Latifur Khan, Philip A. Schrodt, Javier Osorio and James Honaker and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and International Studies Quarterly.

In The Last Decade

Vito D’Orazio

30 papers receiving 536 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.

The MID4 dataset, 2002–2010: Procedures, coding rules and description

2015 201 citations Glenn Palmer, Vito D’Orazio et al. Conflict Management and Peace Science profile →

Peers

Vito D’Orazio

SPS

PSIR

AI

EE

DEVEL

Florian M Hollenbach United States

David R. Mares United States

Luc Girardin Switzerland

ZACHARY STEINERT-THRELKELD United States

Fernando Mendez Switzerland

James Mulvenon United States

Margot E. Kaminski United States

Brandon Valeriano United States

Shanthi Kalathil United States

Uwe Serdült Switzerland

Florian M Hollenbach United States

Vito D’Orazio

292 ×1.0

SPS

175 ×0.8

PSIR

70 ×0.5

AI

132 ×1.8

EE

16 ×0.2

DEVEL

Citations per year, relative to Vito D’Orazio Vito D’Orazio (= 1×) peers Florian M Hollenbach

Countries citing papers authored by Vito D’Orazio

Since Specialization

Citations

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

Fields of papers citing papers by Vito D’Orazio

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Vito D’Orazio. 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 Vito D’Orazio. The network helps show where Vito D’Orazio may publish in the future.

Co-authorship network of co-authors of Vito D’Orazio

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

All Works

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20 of 20 papers shown

1.

Osorio, Javier, et al.. (2024). Keep it Local: Comparing Domain-Specific LLMs in Native and Machine Translated Text using Parallel Corpora on Political Conflict. 542–552.

2.

Khan, Latifur, et al.. (2024). Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification. 583–603. 2 indexed citations

3.

Osorio, Javier, et al.. (2023). ConfliBERT-Spanish: A Pre-trained Spanish Language Model for Political Conflict and Violence. 287–292. 3 indexed citations

4.

D’Orazio, Vito & Yu Lin. (2022). Forecasting conflict in Africa with automated machine learning systems. International Interactions. 48(4). 714–738. 12 indexed citations

5.

Khan, Latifur, et al.. (2021). CoMe-KE: A New Transformers Based Approach for Knowledge Extraction in Conflict and Mediation Domain. 2021 IEEE International Conference on Big Data (Big Data). 1449–1459. 4 indexed citations

6.

Rampin, Rémi, et al.. (2021). An Ecosystem of Applications for Modeling Political Violence. 1. 2384–2388. 3 indexed citations

7.

Palmer, Glenn, et al.. (2021). The MID5 Dataset, 2011–2014: Procedures, coding rules, and description. Conflict Management and Peace Science. 39(4). 470–482. 53 indexed citations

8.

Khan, Latifur, et al.. (2021). 3M-Transformers for Event Coding on Organized Crime Domain. 1–10. 6 indexed citations

9.

D’Orazio, Vito. (2020). Advancing Measurement of Foreign Policy Similarity. SocArXiv (OSF Preprints).

10.

D’Orazio, Vito, et al.. (2019). UTDEventData: An R package to access political event data. The Journal of Open Source Software. 4(36). 1322–1322. 6 indexed citations

11.

D’Orazio, Vito, et al.. (2019). Buying blue helmets: The role of foreign aid in the construction of UN peacekeeping missions. Journal of Peace Research. 57(2). 312–328. 14 indexed citations

12.

D’Orazio, Vito, et al.. (2019). Modeling and Forecasting Armed Conflict: AutoML with Human-Guided Machine Learning. 4714–4723. 5 indexed citations

13.

D’Orazio, Vito, et al.. (2018). TwoRavens for Event Data. 1. 394–401. 4 indexed citations

14.

D’Orazio, Vito & Idean Salehyan. (2018). Who is a Terrorist? Ethnicity, Group Affiliation, and Understandings of Political Violence. International Interactions. 44(6). 1017–1039. 36 indexed citations

15.

Khan, Latifur, et al.. (2017). RePAIR: Recommend political actors in real-time from news websites. 1333–1340. 8 indexed citations

16.

D’Orazio, Vito, et al.. (2016). Crowdsourcing the Measurement of Interstate Conflict. PLoS ONE. 11(6). e0156527–e0156527. 13 indexed citations

17.

D’Orazio, Vito, et al.. (2015). Kickoff to Conflict: A Sequence Analysis of Intra-State Conflict-Preceding Event Structures. PLoS ONE. 10(5). e0122472–e0122472. 10 indexed citations

18.

D’Orazio, Vito, et al.. (2014). Separating the Wheat from the Chaff: Applications of Automated Document Classification Using Support Vector Machines. Political Analysis. 22(2). 224–242. 39 indexed citations

19.

D’Orazio, Vito. (2012). War Games: North Korea'S Reaction to US and South Korean Military Exercises. Journal of East Asian Studies. 12(2). 275–294. 11 indexed citations

20.

D’Orazio, Vito. (2012). War Games: North Korea’s Reaction to American and South Korean Military Exercises. SSRN Electronic Journal. 1 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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