### Publications Published - [1]I. Van der Vegt, M. Mozes, B. Kleinberg, and P. Gill, “The GRIEVANCE dictionary: Understanding threatening language use,” Behavior Research Methods, Feb. 2021, doi: in press. - [2]B. Kleinberg and B. Verschuere, “How humans impair automated deception detection performance,” Acta Psychologica, vol. 213, p. 103250, Feb. 2021, doi: 10.1016/j.actpsy.2020.103250. - [3]I. van der Vegt, M. Mozes, P. Gill, and B. Kleinberg, “Online influence, offline violence: language use on YouTube surrounding the ‘Unite the Right’ rally,” Journal of Computational Social Science, Sep. 2020, doi: 10.1007/s42001-020-00080-x. - [4]I. van der Vegt, B. Kleinberg, and P. Gill, “Too good to be true? Predicting author profiles from abusive language,” arXiv:2009.01126 [cs], Sep. 2020, Accessed: Jan. 25, 2021. [Online]. Available: http://arxiv.org/abs/2009.01126. - [5]B. Kleinberg, I. van der Vegt, and M. Mozes, “Measuring Emotions in the COVID-19 Real World Worry Dataset,” presented at the ACL-NLP-COVID19 2020, Online, Jul. 2020, Accessed: Nov. 02, 2020. [Online]. Available: https://www.aclweb.org/anthology/2020.nlpcovid19-acl.11. - [6]B. Kleinberg, “Manipulating emotions for ground truth emotion analysis,” arXiv:2006.08952 [cs], Jun. 2020, Accessed: Jun. 24, 2020. [Online]. Available: http://arxiv.org/abs/2006.08952. - [7]M. Mozes, P. Stenetorp, B. Kleinberg, and L. D. Griffin, “Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples,” arXiv:2004.05887 [cs], Apr. 2020, Accessed: Apr. 14, 2020. [Online]. Available: http://arxiv.org/abs/2004.05887. - [8]B. Kleinberg and P. McFarlane, “Violent music vs violence and music: Drill rap and violent crime in London,” arXiv:2004.04598 [cs], Apr. 2020, Accessed: Apr. 10, 2020. [Online]. Available: http://arxiv.org/abs/2004.04598. - [9]B. Kleinberg and B. Verschuere, “How human judgment impairs automated deception detection performance,” arXiv:2003.13316 [cs], Mar. 2020, Accessed: Apr. 11, 2020. [Online]. Available: http://arxiv.org/abs/2003.13316. - [10]B. Kleinberg, I. van der Vegt, and P. Gill, “The temporal evolution of a far-right forum,” Journal of Computational Social Science, Feb. 2020, doi: 10.1007/s42001-020-00064-x. - [11]G. Lukács, B. Kleinberg, M. Kunzi, and U. Ansorge, “Response Time Concealed Information Test on Smartphones,” Collabra: Psychology, vol. 6, no. 1, p. 4, Jan. 2020, doi: 10.1525/collabra.255. - [12]A. Volodko, E. Cockbain, and B. Kleinberg, “‘Spotting the signs’ of trafficking recruitment online: Exploring the characteristics of advertisements targeted at migrant job-seekers,” Trends in Organized Crime, 2020, doi: 10.1007/s12117-019-09376-5. - [13]I. van der Vegt and B. Kleinberg, “Women Worry About Family, Men About the Economy: Gender Differences in Emotional Responses to COVID-19,” in Social Informatics, vol. 12467, S. Aref, K. Bontcheva, M. Braghieri, F. Dignum, F. Giannotti, F. Grisolia, and D. Pedreschi, Eds. Cham: Springer, 2020, pp. 397–409. - [14]B. Kleinberg and P. McFarlane, “Examining UK drill music through sentiment trajectory analysis,” arXiv:1911.01324 [cs], Nov. 2019, Accessed: Nov. 22, 2019. [Online]. Available: http://arxiv.org/abs/1911.01324. - [15]B. Kleinberg, I. van der Vegt, A. Arntz, and B. Verschuere, “Detecting deceptive communication through linguistic concreteness,” PsyArXiv, Mar. 2019, doi: 10.31234/osf.io/p3qjh. - [16]I. van der Vegt, P. Gill, S. Macdonald, and B. Kleinberg, “Shedding Light on Terrorist and Extremist Content Removal,” Global Research Network on Terrorism and Technology, 2019, [Online]. Available: https://rusi.org/sites/default/files/20190703_grntt_paper_3.pdf. - [17]F. Soldner, J. C. Ho, M. Makhortykh, I. Van der Vegt, M. Mozes, and B. Kleinberg, “Uphill from here: Sentiment patterns in videos from left- and right-wing YouTube news channels,” presented at the NAACL, 2019. - [18]B. Kleinberg, A. Arntz, and B. Verschuere, “Detecting Deceptive Intentions: Possibilities for Large-Scale Applications,” in The Palgrave Handbook of Deceptive Communication, T. Docan-Morgan, Ed. Cham: Springer International Publishing, 2019, pp. 403–427. - [19]B. Kleinberg, A. Arntz, and B. Verschuere, “Being accurate about accuracy in verbal deception detection,” PLOS ONE, vol. 14, no. 8, p. e0220228, 2019, doi: 10.1371/journal.pone.0220228. - [20]K. Suchotzki, J. De Houwer, B. Kleinberg, and B. Verschuere, “Using more different and more familiar targets improves the detection of concealed information,” Acta Psychologica, vol. 185, pp. 65–71, 2018. - [21]V. Pérez-Rosas, B. Kleinberg, A. Lefevre, and R. Mihalcea, “Automatic Detection of Fake News,” in Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, New Mexico, USA, 2018, pp. 3391–3401, Accessed: Oct. 31, 2018. [Online]. Available: http://aclweb.org/anthology/C18-1287. - [22]B. Kleinberg, L. Warmelink, A. Arntz, and B. Verschuere, “The first direct replication on using verbal credibility assessment for the detection of deceptive intentions,” Applied Cognitive Psychology, vol. 32, no. 5, pp. 592–599, 2018. - [23]B. Kleinberg, Y. Van Der Toolen, A. Vrij, A. Arntz, and B. Verschuere, “Automated verbal credibility assessment of intentions: The model statement technique and predictive modeling,” Applied Cognitive Psychology, vol. 32, no. 3, pp. 354–366, 2018. - [24]B. Kleinberg, Y. van der Toolen, A. Arntz, and B. Verschuere, “Detecting Concealed Information on a Large Scale: Possibilities and Problems,” in Detecting Concealed Information and Deception: Recent Developments, J. P. Rosenfeld, Ed. 2018, p. 377. - [25]B. Kleinberg, M. Mozes, and I. van der Vegt, “Identifying the sentiment styles of YouTube’s vloggers,” Brussels, Belgium, 2018, pp. 3581–3590, [Online]. Available: https://www.aclweb.org/anthology/D18-1394. - [26]B. Kleinberg, M. Mozes, A. Arntz, and B. Verschuere, “Using named entities for computer-automated verbal deception detection,” Journal of Forensic Sciences, vol. 63, no. 3, pp. 714–723, 2018. - [27]J. Kamps and B. Kleinberg, “To the moon: defining and detecting cryptocurrency pump-and-dumps,” Crime Science, vol. 7, no. 1, p. 18, 2018. - [28]B. Verschuere and B. Kleinberg, “Assessing autobiographical memory: the web-based autobiographical Implicit Association Test,” Memory, vol. 25, no. 4, pp. 520–530, 2017. - [29]G. Lukács, B. Kleinberg, and B. Verschuere, “Familiarity-related fillers improve the validity of reaction time-based memory detection,” Journal of Applied Research in Memory and Cognition, vol. 6, no. 3, pp. 295–305, 2017. - [30]B. Kleinberg, G. Nahari, A. Arntz, and B. Verschuere, “An investigation on the detectability of deceptive intent about flying through verbal deception detection,” Collabra: Psychology, vol. 3, no. 1, 2017. - [31]B. Kleinberg, M. Mozes, Y. van der Toolen, and B. Verschuere, “NETANOS-named entity-based text anonymization for open science,” OSF Preprints, vol. 10, 2017. - [32]B. Kleinberg and M. Mozes, “Web-based text anonymization with Node.js: Introducing NETANOS (Named entity-based Text Anonymization for Open Science),” The Journal of Open Source Software, vol. 2, no. 14, 2017. - [33]B. Verschuere and B. Kleinberg, “ID-Check: Online Concealed Information Test Reveals True Identity,” Journal of Forensic Sciences, vol. 61, pp. S237–S240, 2016. - [34]A.-M. Leach, N. Ammar, D. N. England, L. M. Remigio, B. Kleinberg, and B. J. Verschuere, “Less is more? Detecting lies in veiled witnesses.,” Law and Human Behavior, vol. 40, no. 4, p. 401, 2016. - [35]B. Kleinberg and B. Verschuere, “The role of motivation to avoid detection in reaction time-based concealed information detection,” Journal of Applied Research in Memory and Cognition, vol. 5, no. 1, pp. 43–51, 2016. - [36]B. Kleinberg, G. Nahari, and B. Verschuere, “Using the verifiability of details as a test of deception: A conceptual framework for the automation of the verifiability approach,” in Proceedings of the Second Workshop on Computational Approaches to Deception Detection, 2016, pp. 18–25. - [37]B. Kleinberg and B. Verschuere, “Memory Detection 2.0: The First Web-Based Memory Detection Test,” PLOS ONE, vol. 10, no. 4, p. e0118715, Apr. 2015, doi: 10.1371/journal.pone.0118715. - [38]B. Verschuere, B. Kleinberg, and K. Theocharidou, “RT-based memory detection: Item saliency effects in the single-probe and the multiple-probe protocol,” Journal of Applied Research in Memory and Cognition, vol. 4, no. 1, pp. 59–65, 2015. - [39]Open Science Collaboration, “Estimating the reproducibility of psychological science,” Science, vol. 349, no. 6251, p. aac4716, 2015. Popular science - [1]P. McFarlane and B. Kleinberg, “Political Drillin? What machine learning tells us about the reality of drill music,” Policing Insight, Mar. 09, 2020. https://policinginsight.com/features/analysis/political-drillin-what-machine-learning-tells-us-about-the-reality-of-drill-music/ (accessed Mar. 12, 2020). - [2]I. van der Vegt, B. Kleinberg, and P. Gill, “The Temporal Evolution of a Far-Right Forum,” GNET, 2020. https://gnet-research.org/2020/03/05/the-temporal-evolution-of-a-far-right-forum/ (accessed Mar. 12, 2020). - [3]B. Kleinberg, M. Mozes, and T. Davies, “What does it mean to anonymize text? — SAGE Ocean | Big Data, New Tech, Social Science,” SAGE Ocean, Nov. 28, 2019. https://ocean.sagepub.com/blog/what-does-it-mean-to-anonymize-text (accessed Nov. 29, 2019). - [4]B. Kleinberg, M. Mozes, and T. Davies, “Making sensitive text data accessible for computational social science — SAGE Ocean | Big Data, New Tech, Social Science,” SAGE Ocean, Jul. 18, 2019. https://ocean.sagepub.com/blog/making-sensitive-text-data-accessible-for-computational-social-science (accessed Aug. 31, 2019). - [5]B. Kleinberg and B. Verschuere, “Being Honest About Deception Detection: between popular idea and scientific evidence,” Aviation Security International Magazine, Apr. 16, 2019. - [6]J. Kamps and B. Kleinberg, “Cryptocurrency pump-and-dumps,” BioMedCentral - On Society, Jan. 22, 2019. .