For all academic publications (peer-reviewed papers and preprints), see:

Peer-reviewed journal articles

  1. Kleinberg, B., Loconte, R., & Verschuere, B. (2025). Effective faking of verbal deception detection with target‐aligned adversarial attacks. Legal and Criminological Psychology, lcrp.70001. https://doi.org/10.1111/lcrp.70001
  2. Loconte, R., & Kleinberg, B. (2025). Examining embedded lies through computational text analysis. Scientific Reports, 15(1), 26482. https://doi.org/10.1038/s41598-025-11327-w
  3. Lukács, G., Kleinberg, B., Fekete, A., & Matsuda, I. (2025). A Filtering Response Time Concealed Information Test for Searching for Relevant Concealed Items. Applied Cognitive Psychology, 39(5), e70114. https://doi.org/10.1002/acp.70114
  4. Norouzi, R., Kleinberg, B., Vermunt, J. K., & Van Lissa, C. J. (2025). Capturing causal claims: A fine-tuned text mining model for extracting causal sentences from social science papers. Research Synthesis Methods, 1–18. https://doi.org/10.1017/rsm.2024.13
  5. Peereboom, S., Schwabe, I., & Kleinberg, B. (2025). Cognitive phantoms in large language models through the lens of latent variables. Computers in Human Behavior: Artificial Humans, 4, 100161. https://doi.org/10.1016/j.chbah.2025.100161
  6. Stavrova, O., Kleinberg, B., Evans, A. M., & Ivanović, M. (2025). Scientific publications that use promotional language in the abstract receive more citations and public attention. Communications Psychology, 3(1), 118. https://doi.org/10.1038/s44271-025-00293-8
  7. Loconte, R., Kleinberg, B., & Monaro, M. (2024). Quali metodi per la valutazione dei Large Language Models (LLM)? Riflessioni sull’uso di un approccio neuropsicologico e sullo sviluppo di una psicometria dei LLM. Giornale italiano di psicologia, 3, 579–584. https://doi.org/10.1421/114432
  8. Stavrova, O., Kleinberg, B., Evans, A. M., & Ivanović, M. (2024). Expressions of uncertainty in online science communication hinder information diffusion. PNAS Nexus, 3(10), pgae439. https://doi.org/10.1093/pnasnexus/pgae439
  9. Trozze, A., Davies, T., & Kleinberg, B. (2024). Large language models in cryptocurrency securities cases: Can a GPT model meaningfully assist lawyers? Artificial Intelligence and Law. https://doi.org/10.1007/s10506-024-09399-6
  10. Trozze, A., Kleinberg, B., & Davies, T. (2024). Detecting DeFi securities violations from token smart contract code. Financial Innovation, 10(1), 78. https://doi.org/10.1186/s40854-023-00572-5
  11. Bergmans, B. J. M., Gebeyehu, B. Y., Van Puijenbroek, E. P., Van Deun, K., Kleinberg, B., Murk, J.-L., & De Vries, E. (2023). Infections in Biological and Targeted Synthetic Drug Use in Rheumatoid Arthritis: Where do We Stand? A Scoping Review and Meta-analysis. Rheumatology and Therapy. https://doi.org/10.1007/s40744-023-00571-z
  12. Bray, S. D., Johnson, S. D., & Kleinberg, B. (2023). Testing human ability to detect ‘deepfake’ images of human faces. Journal of Cybersecurity, 9(1), tyad011. https://doi.org/10.1093/cybsec/tyad011
  13. Soldner, F., Kleinberg, B., & Johnson, S. D. (2023). Counterfeits on dark markets: A measurement between Jan-2014 and Sep-2015. Crime Science, 12(1), 18. https://doi.org/10.1186/s40163-023-00195-2
  14. Trozze, A., Davies, T., & Kleinberg, B. (2023). Of degens and defrauders: Using open-source investigative tools to investigate decentralized finance frauds and money laundering. Forensic Science International: Digital Investigation, 46, 301575. https://doi.org/10.1016/j.fsidi.2023.301575
  15. Van Der Vegt, I., & Kleinberg, B. (2023). A multi-modal panel dataset to understand the psychological impact of the pandemic. Scientific Data, 10(1), 537. https://doi.org/10.1038/s41597-023-02438-y
  16. van der Vegt, I., Kleinberg, B., & Gill, P. (2023). Proceed with caution: On the use of computational linguistics in threat assessment. Journal of Policing, Intelligence and Counter Terrorism, 1–9. https://doi.org/10.1080/18335330.2023.2165137
  17. Verschuere, B., Lin, C.-C., Huismann, S., Kleinberg, B., Willemse, M., Mei, E. C. J., Van Goor, T., Löwy, L. H. S., Appiah, O. K., & Meijer, E. (2023). The use-the-best heuristic facilitates deception detection. Nature Human Behaviour, 7(5), 718–728. https://doi.org/10.1038/s41562-023-01556-2
  18. Hoogeveen, S., Sarafoglou, A., Aczel, B., Aditya, Y., Alayan, A. J., Allen, P. J., Altay, S., Alzahawi, S., Amir, Y., Anthony, F.-V., Kwame Appiah, O., Atkinson, Q. D., Baimel, A., Balkaya-Ince, M., Balsamo, M., Banker, S., Bartoš, F., Becerra, M., Beffara, B., … Wagenmakers, E.-J. (2022). A many-analysts approach to the relation between religiosity and well-being. Religion, Brain & Behavior, 1–47. https://doi.org/10.1080/2153599X.2022.2070255
  19. Soldner, F., Kleinberg, B., & Johnson, S. D. (2022). Confounds and overestimations in fake review detection: Experimentally controlling for product-ownership and data-origin. PLOS ONE, 17(12), e0277869. https://doi.org/10.1371/journal.pone.0277869
  20. Trozze, A., Davies, T., & Kleinberg, B. (2022). Explaining prosecution outcomes for cryptocurrency-based financial crimes. Journal of Money Laundering Control. https://doi.org/10.1108/JMLC-10-2021-0119
  21. Trozze, A., Kamps, J., Akartuna, E. A., Hetzel, F. J., Kleinberg, B., Davies, T., & Johnson, S. D. (2022). Cryptocurrencies and future financial crime. Crime Science, 11(1), 1. https://doi.org/10.1186/s40163-021-00163-8
  22. van der Vegt, I., Gregory, P., van der Meer, B. B., Yang, J., Kleinberg, B., & Gill, P. (2022). Assessment procedures in anonymously written threats of harm and violence. Journal of Threat Assessment and Management. https://doi.org/10.1037/tam0000168
  23. van der Vegt, I., Kleinberg, B., & Gill, P. (2022). Predicting author profiles from online abuse directed at public figures. Journal of Threat Assessment and Management. https://doi.org/10.1037/tam0000172
  24. Kleinberg, B., & Verschuere, B. (2021). How humans impair automated deception detection performance. Acta Psychologica, 213, 103250. https://doi.org/10.1016/j.actpsy.2020.103250
  25. Mozes, M., van der Vegt, I., & Kleinberg, B. (2021). A repeated-measures study on emotional responses after a year in the pandemic. Scientific Reports, 11(1), 23114. https://doi.org/10.1038/s41598-021-02414-9
  26. Schweinsberg, M., Feldman, M., Staub, N., […] Kleinberg, B. […] Luis Uhlmann, E. (2021). Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis. Organizational Behavior and Human Decision Processes. https://doi.org/10.1016/j.obhdp.2021.02.003
  27. van der Vegt, I., Mozes, M., Gill, P., & Kleinberg, B. (2021). Online influence, offline violence: Language use on YouTube surrounding the ‘Unite the Right’ rally. Journal of Computational Social Science, 4(1), 333–354. https://doi.org/10.1007/s42001-020-00080-x
  28. van der Vegt, I., Mozes, M., Kleinberg, B., & Gill, P. (2021). The Grievance Dictionary: Understanding threatening language use. Behavior Research Methods. https://doi.org/10.3758/s13428-021-01536-2
  29. Kleinberg, B., van der Vegt, I., & Gill, P. (2020). The temporal evolution of a far-right forum. Journal of Computational Social Science. https://doi.org/10.1007/s42001-020-00064-x
  30. Lukács, G., Kleinberg, B., Kunzi, M., & Ansorge, U. (2020). Response Time Concealed Information Test on Smartphones. Collabra: Psychology, 6(1), 4. https://doi.org/10.1525/collabra.255
  31. Volodko, A., Cockbain, E., & Kleinberg, B. (2020). “Spotting the signs” of trafficking recruitment online: Exploring the characteristics of advertisements targeted at migrant job-seekers. Trends in Organized Crime. https://doi.org/10.1007/s12117-019-09376-5
  32. Kleinberg, B., Arntz, A., & Verschuere, B. (2019). Being accurate about accuracy in verbal deception detection. PLOS ONE, 14(8), e0220228. https://doi.org/10.1371/journal.pone.0220228
  33. van der Vegt, I., Gill, P., Macdonald, S., & Kleinberg, B. (2019). Shedding Light on Terrorist and Extremist Content Removal. Global Research Network on Terrorism and Technology. https://rusi.org/sites/default/files/20190703_grntt_paper_3.pdf
  34. Kamps, J., & Kleinberg, B. (2018). To the moon: Defining and detecting cryptocurrency pump-and-dumps. Crime Science, 7(1), 18.
  35. Kleinberg, B., Mozes, M., Arntz, A., & Verschuere, B. (2018). Using named entities for computer-automated verbal deception detection. Journal of Forensic Sciences, 63(3), 714–723.
  36. Kleinberg, B., Van Der Toolen, Y., Vrij, A., Arntz, A., & Verschuere, B. (2018). Automated verbal credibility assessment of intentions: The model statement technique and predictive modeling. Applied Cognitive Psychology, 32(3), 354–366.
  37. Kleinberg, B., Warmelink, L., Arntz, A., & Verschuere, B. (2018). The first direct replication on using verbal credibility assessment for the detection of deceptive intentions. Applied Cognitive Psychology, 32(5), 592–599.
  38. Suchotzki, K., De Houwer, J., Kleinberg, B., & Verschuere, B. (2018). Using more different and more familiar targets improves the detection of concealed information. Acta Psychologica, 185, 65–71.
  39. Kleinberg, B., & Mozes, M. (2017). Web-based text anonymization with Node.js: Introducing NETANOS (Named entity-based Text Anonymization for Open Science). The Journal of Open Source Software, 2(14).
  40. Kleinberg, B., Nahari, G., Arntz, A., & Verschuere, B. (2017). An investigation on the detectability of deceptive intent about flying through verbal deception detection. Collabra: Psychology, 3(1).
  41. Lukács, G., Kleinberg, B., & Verschuere, B. (2017). Familiarity-related fillers improve the validity of reaction time-based memory detection. Journal of Applied Research in Memory and Cognition, 6(3), 295–305.
  42. Verschuere, B., & Kleinberg, B. (2017). Assessing autobiographical memory: The web-based autobiographical Implicit Association Test. Memory, 25(4), 520–530.
  43. Kleinberg, B., & Verschuere, B. (2016). The role of motivation to avoid detection in reaction time-based concealed information detection. Journal of Applied Research in Memory and Cognition, 5(1), 43–51.
  44. Leach, A.-M., Ammar, N., England, D. N., Remigio, L. M., Kleinberg, B., & Verschuere, B. J. (2016). Less is more? Detecting lies in veiled witnesses. Law and Human Behavior, 40(4), 401.
  45. Verschuere, B., & Kleinberg, B. (2016). ID-Check: Online Concealed Information Test Reveals True Identity. Journal of Forensic Sciences, 61, S237–S240.
  46. Kleinberg, B., & Verschuere, B. (2015). Memory Detection 2.0: The First Web-Based Memory Detection Test. PLOS ONE, 10(4), e0118715. https://doi.org/10.1371/journal.pone.0118715
  47. Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716.
  48. Verschuere, B., Kleinberg, B., & Theocharidou, K. (2015). RT-based memory detection: Item saliency effects in the single-probe and the multiple-probe protocol. Journal of Applied Research in Memory and Cognition, 4(1), 59–65.

Peer-reviewed conference papers

Note: conference proceedings papers are the premier outlet for research in Natural Language Processing and computational sciences and have a competitive peer-reviewing process.

  1. Emmery, C., Miotto, M., Kramp, S., & Kleinberg, B. (2024). SOBR: A Corpus for Stylometry, Obfuscation, and Bias on Reddit. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Eds.), Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 14967–14983). ELRA and ICCL. https://aclanthology.org/2024.lrec-main.1302
  2. Griffin, L., Kleinberg, B., Mozes, M., Mai, K., Vau, M. D. M., Caldwell, M., & Mavor-Parker, A. (2023). Large Language Models respond to Influence like Humans. Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023), 15–24. https://doi.org/10.18653/v1/2023.sicon-1.3
  3. Miotto, M., Rossberg, N., & Kleinberg, B. (2022). Who is GPT-3? An exploration of personality, values and demographics. Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS), 218–227. https://aclanthology.org/2022.nlpcss-1.24
  4. Mozes, M., Kleinberg, B., & Griffin, L. (2022). Identifying Human Strategies for Generating Word-Level Adversarial Examples. Findings of the Association for Computational Linguistics: EMNLP 2022, 6118–6126. https://aclanthology.org/2022.findings-emnlp.454
  5. Mozes, M., Bartolo, M., Stenetorp, P., Kleinberg, B., & Griffin, L. (2021). Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 8258–8270. https://aclanthology.org/2021.emnlp-main.651
  6. Mozes, M., Stenetorp, P., Kleinberg, B., & Griffin, L. (2021). Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 171–186. https://www.aclweb.org/anthology/2021.eacl-main.13
  7. van der Vegt, I., & Kleinberg, B. (2020). Women Worry About Family, Men About the Economy: Gender Differences in Emotional Responses to COVID-19. Social Informatics, 12467, 397–409. https://doi.org/10.1007/978-3-030-60975-7_29
  8. Kleinberg, B., van der Vegt, I., & Mozes, M. (2020, July). Measuring Emotions in the COVID-19 Real World Worry Dataset. Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020. ACL-NLP-COVID19 2020, Online. https://www.aclweb.org/anthology/2020.nlpcovid19-acl.11
  9. Soldner, F., Ho, J. C., Makhortykh, M., van der Vegt, I., Mozes, M., & Kleinberg, B. (2019). Uphill from here: Sentiment patterns in videos from left- and right-wing YouTube news channels. Workshop on Natural Language Processing and Computational Social Science. NAACL.
  10. Kleinberg, B., Mozes, M., & van der Vegt, I. (2018). Identifying the sentiment styles of YouTube’s vloggers. 3581–3590. https://www.aclweb.org/anthology/D18-1394
  11. Pérez-Rosas, V., Kleinberg, B., Lefevre, A., & Mihalcea, R. (2018). Automatic Detection of Fake News. Proceedings of the 27th International Conference on Computational Linguistics, 3391–3401. http://aclweb.org/anthology/C18-1287
  12. Kleinberg, B., Nahari, G., & Verschuere, B. (2016). Using the verifiability of details as a test of deception: A conceptual framework for the automation of the verifiability approach. Proceedings of the Second Workshop on Computational Approaches to Deception Detection, 18–25.

Pre-prints

  1. Bosten, M., & Kleinberg, B. (2025). Conflicts of Interest in Published NLP Research 2000–2024 (No. arXiv:2502.16218). arXiv. https://doi.org/10.48550/arXiv.2502.16218
  2. Rossberg, N., Kleinberg, B., O’Sullivan, B., Longo, L., & Visentin, A. (2025). The Feature Understandability Scale for Human-Centred Explainable AI: Assessing Tabular Feature Importance (No. arXiv:2510.07050). arXiv. https://doi.org/10.48550/arXiv.2510.07050
  3. van der Vegt, I., Kleinberg, B., Miotto, M., & Festor, J. (2025). Translating the Grievance Dictionary: A psychometric evaluation of Dutch, German, and Italian versions (No. arXiv:2505.07495). arXiv. https://doi.org/10.48550/arXiv.2505.07495
  4. Verschuere, B., Kleinberg, B., Van Kolfschooten, M., Bolhoven, L., & Rassin, E. (2025). How Dutch legal professionals assess statement credibility: Evidence from a survey and an analysis of 518 court rulings on sexual abuse. Open Science Framework. https://doi.org/10.31219/osf.io/grj5c_v1
  5. Kleinberg, B., Zegers, J., Festor, J., Vida, S., Präsent, J., Loconte, R., & Peereboom, S. (2024). Trying to be human: Linguistic traces of stochastic empathy in language models (No. arXiv:2410.01675). arXiv. https://doi.org/10.48550/arXiv.2410.01675
  6. Soldner, F., Plum, F., Kleinberg, B., & Johnson, S. D. (2024). From cryptomarkets to the surface web: Scouting eBay for counterfeits (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2406.05021
  7. Zegers, J., & Kleinberg, B. (2024). Beyond the sample: Individual differences in psychological responses to the COVID-19 pandemic. https://doi.org/10.31234/osf.io/2px7h
  8. Mozes, M., He, X., Kleinberg, B., & Griffin, L. D. (2023). Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities (No. arXiv:2308.12833). arXiv. http://arxiv.org/abs/2308.12833
  9. Ilias, L., Soldner, F., & Kleinberg, B. (2022). Explainable Verbal Deception Detection using Transformers. https://doi.org/10.48550/ARXIV.2210.03080
  10. Kleinberg, B., Davies, T., & Mozes, M. (2022). Textwash—Automated open-source text anonymisation. arXiv:2208.13081. https://doi.org/10.48550/arXiv.2208.13081
  11. Mozes, M., & Kleinberg, B. (2021). No Intruder, no Validity: Evaluation Criteria for Privacy-Preserving Text Anonymization. arXiv:2103.09263 [Cs]. http://arxiv.org/abs/2103.09263
  12. Kleinberg, B. (2020). Manipulating emotions for ground truth emotion analysis. arXiv:2006.08952 [Cs]. http://arxiv.org/abs/2006.08952
  13. Kleinberg, B., & McFarlane, P. (2020). Violent music vs violence and music: Drill rap and violent crime in London. arXiv:2004.04598 [Cs]. http://arxiv.org/abs/2004.04598
  14. Kleinberg, B., & McFarlane, P. (2019). Examining UK drill music through sentiment trajectory analysis. arXiv:1911.01324 [Cs]. http://arxiv.org/abs/1911.01324 15. Kleinberg, B., van der Vegt, I., Arntz, A., & Verschuere, B. (2019). Detecting deceptive communication through linguistic concreteness. PsyArXiv. https://doi.org/10.31234/osf.io/p3qjh
  15. Kleinberg, B., Mozes, M., van der Toolen, Y., & Verschuere, B. (2017). NETANOS—Named entity-based text anonymization for open science. OSF Preprints, 10.

Book chapters

  1. Kamps, J., Trozze, A., & Kleinberg, B. (2022). Cryptocurrencies: Boons and curses for fraud prevention. In Y. Hanoch & S. Wood (Eds.), A fresh look at fraud: Theoretical and applied perspectives. Routledge.
  2. Soldner, F., Kleinberg, B., & Johnson, S. (2022). Trends in online consumer fraud: A data science perspective. In Y. Hanoch & S. Wood (Eds.), A fresh look at fraud: Theoretical and applied perspectives. Routledge.
  3. Van der Vegt, I., Kleinberg, B., & Gill, P. (2022). Understanding Lone-Actor Violence through Linguistic Analysis. In J. C. Holzer, A. J. Dew, P. R. Recupero, & P. Gill (Eds.), Lone-actor terrorism: An integrated framework. Oxford University Press.
  4. Kleinberg, B., Arntz, A., & Verschuere, B. (2019). Detecting Deceptive Intentions: Possibilities for Large-Scale Applications. In T. Docan-Morgan (Ed.), The Palgrave Handbook of Deceptive Communication (pp. 403–427). Springer International Publishing. https://doi.org/10.1007/978-3-319-96334-1_21
  5. Kleinberg, B., van der Toolen, Y., Arntz, A., & Verschuere, B. (2018). Detecting Concealed Information on a Large Scale: Possibilities and Problems. In J. P. Rosenfeld (Ed.), Detecting Concealed Information and Deception: Recent Developments (p. 377).

Other reports/briefings

  1. Akartuna, E. A., Hetzel, F. J., & Kleinberg, B. (2021). Policy brief: Cryptocurrencies and future crime (Dawes Centre for Future Crime) [Policy Brief].
  2. Royal United Services Institute & (RUSI) for Defence and Security Studies. (2021). A Complex Matter: Examining Reporting on Terrorism in the UK. https://static.rusi.org/256_op_media_and_terrorism_web_0.pdf
  3. Kleinberg, B., Griffin, L. D., Rottweiler, B., Miotto, M., Rossberg, N., Praesent, J., & Gal, J. (n.d.). Targeted shifting of attitudes towards women with LLM-generated arguments. In Preparation.
  4. Kleinberg, B., Präsent, J., Gal, J., & Griffin, L. D. (n.d.). Learning to convince from another AI: reinforcement-based argument generation. In Preparation.

Software

  1. Kleinberg, B. (2024). rgpt3: Making requests from R to the GPT API (Version 1.0) [Computer software]. https://doi.org/10.5281/zenodo.7327667
  2. Mozes, M., & Kleinberg, B. (2023). Textwash: Automated, Learning-Based Text Anonymisation [Computer software]. https://github.com/maximilianmozes/textwash
  3. Mozes, M., & Kleinberg, B. (2022). netanos: Named Entity-based Text ANonymization for Open Science [Computer software]. https://www.npmjs.com/package/netanos
  4. Lukács, G., van Doorn, J., & Kleinberg, B. (2021). neatStats: Neat and Painless Statistical Reporting [Computer software]. https://cran.r-project.org/web/packages/neatStats/index.html

List last updated: November 2025