Samuel Mascarenhas

1.9k total citations
58 papers, 861 citations indexed

About

Samuel Mascarenhas is a scholar working on Social Psychology, Artificial Intelligence and Sociology and Political Science. According to data from OpenAlex, Samuel Mascarenhas has authored 58 papers receiving a total of 861 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Social Psychology, 29 papers in Artificial Intelligence and 16 papers in Sociology and Political Science. Recurrent topics in Samuel Mascarenhas's work include Social Robot Interaction and HRI (29 papers), Multi-Agent Systems and Negotiation (10 papers) and Artificial Intelligence in Games (9 papers). Samuel Mascarenhas is often cited by papers focused on Social Robot Interaction and HRI (29 papers), Multi-Agent Systems and Negotiation (10 papers) and Artificial Intelligence in Games (9 papers). Samuel Mascarenhas collaborates with scholars based in Portugal, United States and Australia. Samuel Mascarenhas's co-authors include Ana Paiva, Rui Prada, Iolanda Leite, Carlos Martinho, André Pereira, João Dias, Filipa Correia, Francisco S. Melo, Pedro A. Santos and Fernando P. Santos and has published in prestigious journals such as Computers in Human Behavior, International Journal of Human-Computer Studies and Education and Information Technologies.

In The Last Decade

Samuel Mascarenhas

53 papers receiving 814 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Samuel Mascarenhas Portugal 15 483 357 180 131 117 58 861
Astrid Rosenthal-von der Pütten Germany 17 746 1.5× 412 1.2× 264 1.5× 325 2.5× 120 1.0× 48 1.2k
Cristen Torrey United States 9 571 1.2× 380 1.1× 111 0.6× 128 1.0× 146 1.2× 14 854
Martina Mara Austria 18 469 1.0× 384 1.1× 166 0.9× 187 1.4× 108 0.9× 45 942
Solace Shen United States 10 615 1.3× 301 0.8× 94 0.5× 252 1.9× 116 1.0× 22 887
Kensuke Kato Japan 8 657 1.4× 389 1.1× 120 0.7× 155 1.2× 73 0.6× 17 884
Sin‐Hwa Kang United States 12 344 0.7× 206 0.6× 155 0.9× 97 0.7× 219 1.9× 30 653
Dieta Kuchenbrandt Germany 13 710 1.5× 386 1.1× 260 1.4× 281 2.1× 91 0.8× 17 917
Victoria Groom United States 10 456 0.9× 193 0.5× 166 0.9× 121 0.9× 168 1.4× 17 687
Jakub Złotowski New Zealand 13 726 1.5× 504 1.4× 204 1.1× 318 2.4× 132 1.1× 19 1.1k
Jan‐Philipp Stein Germany 13 283 0.6× 232 0.6× 388 2.2× 152 1.2× 117 1.0× 33 939

Countries citing papers authored by Samuel Mascarenhas

Since Specialization
Citations

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

Fields of papers citing papers by Samuel Mascarenhas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Samuel Mascarenhas

This figure shows the co-authorship network connecting the top 25 collaborators of Samuel Mascarenhas. A scholar is included among the top collaborators of Samuel Mascarenhas 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 Samuel Mascarenhas. Samuel Mascarenhas 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.
Mascarenhas, Samuel, et al.. (2025). Ovarian Cancer Screening: Recommendations and Future Prospects. RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren. 197(12). 1395–1404.
2.
Richards, Deborah, et al.. (2021). Verbal empathy and explanation to encourage behaviour change intention. Journal on Multimodal User Interfaces. 15(2). 189–199. 9 indexed citations
3.
Richards, Deborah, et al.. (2020). User-Models to Drive an Adaptive Virtual Advisor: Demonstration. Adaptive Agents and Multi-Agents Systems. 2117–2119. 1 indexed citations
4.
Richards, Deborah, et al.. (2020). Adapting a Virtual Advisor’s Verbal Conversation Based on Predicted User Preferences: A Study of Neutral, Empathic and Tailored Dialogue. Multimodal Technologies and Interaction. 4(3). 55–55. 9 indexed citations
5.
Santos, Fernando P., Samuel Mascarenhas, Francisco C. Santos, et al.. (2019). Outcome-based Partner Selection in Collective Risk Dilemmas. Adaptive Agents and Multi-Agents Systems. 1556–1564. 9 indexed citations
6.
Mascarenhas, Samuel, et al.. (2019). An Accessible Toolkit for the Creation of Socio-EmotionalAgents. Adaptive Agents and Multi-Agents Systems. 2357–2359. 2 indexed citations
7.
Paiva, Ana, et al.. (2019). Social Power in Human-Robot Interaction: Towards More Persuasive Robots. Adaptive Agents and Multi-Agents Systems. 2015–2017. 4 indexed citations
8.
Correia, Filipa, Samuel Mascarenhas, Samuel Gomes, et al.. (2019). Exploring Prosociality in Human-Robot Teams. 143–151. 30 indexed citations
9.
Richards, Deborah, et al.. (2018). Sarah the Virtual Advisor to Reduce Study Stress. UTS ePRESS (University of Technology Sydney). 1829–1831. 2 indexed citations
10.
Correia, Filipa, et al.. (2018). Exploring the Impact of Fault Justification in Human-Robot Trust. Adaptive Agents and Multi-Agents Systems. 507–513. 32 indexed citations
11.
Richards, Deborah, et al.. (2018). Towards an Adaptive System: Users’ Preferences and Responses to an Intelligent Virtual Advisor based on Individual Differences. UTS ePRESS (University of Technology Sydney). 4 indexed citations
12.
Jonell, Patrik, Dimosthenis Kontogiorgos, José Lopes, et al.. (2018). FARMI: A Framework for Recording Multi-Modal Interactions. Language Resources and Evaluation. 3969–3974. 4 indexed citations
13.
Melo, Francisco S., Samuel Mascarenhas, & Ana Paiva. (2018). A tutorial on machine learning for 
interactive pedagogical systems. International Journal of Serious Games. 5(3). 79–112. 4 indexed citations
14.
Mascarenhas, Samuel, et al.. (2013). A Model of Social Dynamics for Social Intelligent Agents. National Conference on Artificial Intelligence. 1(1). 39–46.
15.
Hofstede, Gert Jan, Samuel Mascarenhas, A. Silva, et al.. (2013). Traveller–Intercultural training with intelligent agents for young adults. Socio-Environmental Systems Modeling. 5 indexed citations
16.
Mascarenhas, Samuel, et al.. (2013). "Can I ask you a favour?": a relational model of socio-cultural behaviour. Adaptive Agents and Multi-Agents Systems. 1335–1336. 3 indexed citations
17.
Mascarenhas, Samuel, Ana Paiva, Gennaro Di Tosto, et al.. (2013). An Agent Model for the Appraisal of Normative Events Based in In-Group and Out-Group Relations. Proceedings of the AAAI Conference on Artificial Intelligence. 27(1). 1220–1226. 8 indexed citations
18.
Pereira, André, Iolanda Leite, Samuel Mascarenhas, Carlos Martinho, & Ana Paiva. (2012). Using Empathy to Improve Human-Robot Relationships. 2 indexed citations
19.
Mascarenhas, Samuel, João Dias, Nuno Afonso, Sibylle Enz, & Ana Paiva. (2009). Using rituals to express cultural differences in synthetic characters. Adaptive Agents and Multi-Agents Systems. 305–312. 25 indexed citations
20.
Mascarenhas, Samuel, et al.. (2009). “I can feel it too!”: Emergent empathic reactions between synthetic characters. 1–7. 28 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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