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README.md

I am an Assistant Professor in Data Science at the Department of Security and Crime Science and the Dawes Centre for Future Crime at University College London. I also hold a guest affiliation at the University of Amsterdam's Department of Psychology.

I'm interested in solving and understanding (future) crime and security problems with computational techniques. My research uses a mix of open and closed-source data, and analytical and methodological approaches from natural language processing, machine learning, experimental research and simulation studies. All of my research is aimed at solving problems in the real world.

Current research questions include:

  • Dynamic modelling of sentiment in text
  • Hybrid decision-making approaches in uncertain environments (human-in-the-loop, i.e. integrating machine learning and human expertise)
  • Leading indicators for violent crimes through social media conflict
  • Automated detection of hate speech targets
  • Identifying early warning signals to detect emerging crime trends
  • Understanding and predicting the temporal trajectories of language on online forums
  • Cryptocurrency pump-and-dump detection and mitigation
  • Dynamic, adaptive information elicitation for large-scale passenger screening

Methodological topics:

  • Fundamental data science, esp. the validity of online sensor data (e.g., forum data, dark web data)
  • The stability of computational approaches (e.g., class-imbalance problems, context dependency of machine learning, bad label propagation for prediction models)
  • New ways of generating data for crime research (e.g., simulation approaches, web-scraping)

News

  • 3/2019 Session on fraud I'm running a session of cryptocurrency fraud at the annual Dutch meeting of fraud investigators in The Hague in April 2019
  • 3/2019 Conference talks I'll talk about the potential and problem of data science for crime research at the 2019 Advances in Data Science conference
  • 3/2019 New papers on the accuracy of verbal credibility assessment and on modelling linguistic concreteness for deception detection
  • 2/2019 Conference talks I'll give a talk "Are cryptocurrencies the future of fraud?" at the Policing 2.0 conference in London in March 2019.
  • 1/2019 PhD position on crypto fraud - fully funded 3 or 4 years - we've set a deadline to the 1st of March - more info here
  • 1/2019 Blog post on our crypto fraud paper on BMC
  • 12/2018 Paper on crypto fraud Josh Kamps and I published a paper on cryptocurrency fraud as a crime science problem
  • 12/2018 Workshop at EuroCSS Isabelle van der Vegt (UCL), Maximilian Mozes (TU Munich) and I gave a tutorial workshop on Linguistic Temporal Trajectory Analysis in R [companion website] at this year's [EuroCSS symposium]
  • 11/2018 Postdoc position We are hiring a post-doc for a scoping study on cryptocurrency fraud as an emerging crime problem (upd.: position filled).
  • 10/2018 Conference talks Maximilian Mozes will present our work on sentiment shapes on YouTube at [EuroCSS website] and I will talk about about joint work with Paul Gill and Isabelle van der Vegt on predicting phase transitions of abusive language adoption
  • 09/2018 EMNLP paper on sentiment styles in 27k YouTube vlogs To appear at EMNLP 2018 in Brussels. [paper] [data] [code]
  • 09/2018 Joint project with Greater Manchester Police Together with Reka Solymosi we will work on threat scanning using data science techniques.

Selected recent publications

All publications can be found on my Google Scholar page.

Software

  • NETANOS - Named entity-based text anonymization for open science
    • Anonymises bunches of text files by removing identifiable information
    • de-identifies numbers, persons, locations, places, pronouns, dates and times
    • Developed with Maximilian Mozes and Bruno Verschuere
    • Available on npm, source code on GitHub
    • Published paper in JOSS and experimental validation on OSF servers
    • Accessible interface version lives on netanos.io

Example:

var input = "Max and Ben spent more than 1000 hours on writing the software. They started in August 2016 in Amsterdam.";

netanos.anon(input, function(output) {
    console.log(output);
});

/*
"[PERSON_1] and [PERSON_2] spent more than [DATE/TIME_1] on writing the software. They started in [DATE/TIME_2] in [LOCATION_1]."
*/
  • Naive context sentiment analysis
    • Performs sentiment analysis that handles valence shifters (e.g., "really", "not", "hardly", "but") on non-punctuated or poorly punctuated data
    • Code lives on GitHub
    • Specific use cases:
      • data that are not punctuated at all (e.g., auto-generated YouTube transcripts)
      • data that are badly punctuated (e.g., data from blogs where punctuation is not necessarily a given)
      • very brief text data: Twitter data, for example, even if properly annotated for sentence-boundary-disambiguation, would return one or two sentiment values with other sentiment extraction packages
    • Contributors: Maximilian Mozes and Isabelle van der Vegt

From the README:

#3. USAGE EXAMPLE
##for texts from source, use the function: https://github.com/ben-aaron188/r_helper_functions/blob/master/txt_df_from_dir.R
# data = data.frame('text' = character(3)
#                   , 'text_id' = character(3))
# data$text = c('this is a super, great positive sentence and I just love doing this. Now this will be very negative and with disgusting words and ugly phrases'
#               , 'here we begin in a bad, bad, and ugly way but quickly become overly positive for all the great things this exciting code can do'
#               , "I haven't been sad in a long time. I am extremely happy today. It's a good day. But suddenly I'm only a little bit happy. Then I'm not happy at all. In fact, I am now the least happy person on the planet. There is no happiness left in me. Wait, it's returned! I don't feel so bad after all!")
# data$text_id = c('text1', 'text2', 'text3')
#
# ncs_full(txt_input_col = data$text
#          , txt_id_col = data$text_id
#          , low_pass_filter_size = 5
#          , transform_values = T
#          , normalize_values = F
#          , min_tokens = 10
#          , cluster_lower = 2
#          , cluster_upper = 2
#          )

### END

About me

Brief CV

  • PhD on detecting deceptive intentions on a large scale (Department of Psychology, University of Amsterdam) [2018]
  • MSc in Crime Science (University College London) [2015]
  • BSc in Psychology - specialised in Psychological Methods (University of Amsterdam) [2014]

Links

Teaching 2018/2019

Possible topics for (UG & PG) dissertations and internships 2018/2019:

  • testing the reliability and validity of online sensor data (e.g., Twitter)
  • detecting massive online missinformation (e.g., fake news, polarised content)
  • deception detection (computer-automated or computer-aided)
  • detecting early warning signals of emerging crimes
  • understanding linguistic trajectories in online forums
  • computational approaches to existing and new crime problems
  • methodological research (e.g., dealing with new forms of data, statistical simulation studies)
  • dark crime data (i.e., hidden data, messy data, multimodal data)
  • special topic on financial fraud in collab. with a major internarional bank (contact me for more details)

CURRENT MODULES

  • Probability, Statistics and Modelling 2 (2nd year, BSc in Crime Science, UCL)
    • Includes Bayesian inference, discrete multivariate analysis, GLM
    • Open science lab (reproducibility, questionable research practices)
    • Materials for this module on GitHub and on the companion website.
  • Data Science for crime scientists (3rd year, BSc in Crime Science, UCL)
    • Includes: web-scraping, text mining in R, (un)supervised machine learning
    • Materials for this module on GitHub and on the companion website.
  • Crime Science (honours module, UvA)
    • Module description on UvA website
    • Note: we have a long wait list at the moment
    • Materials/handbook/etc. to be made available here asap.

Past modules

  • Crime Science, BSc module, Institute for Inderdisciplinary Studies, University of Amsterdam, Sem. 2, 2017/2018
  • Deception and Lie Detection, MSc module, Department of Psychology, University of Amsterdam, Sem. 1 2017/2018
  • Crime Science, BSc module, Institute for Inderdisciplinary Studies, University of Amsterdam, Sem. 1, 2017/2018
  • Deception and Lie Detection, MSc module, Department of Psychology, University of Amsterdam, Sem. 1 2016/2017
  • Deception and Lie Detection, MSc module, Department of Psychology, University of Amsterdam, Sem. 1 2015/2016 )

Review service

  • Scientific Reports (Nature), Memory & Cognition, Applied Cognitive Psychology, Computers in Human Behavior, Acta Psychologica, Personality and Individual Differences, Current Psychology, Cognitive Research: Principles and Implications, Crime Science

PhDs & students

Maximilian Mozes

  • PhD student (2019-2022)
  • Topic: Adversarial perturbations in security algorithms
  • Supervised with Dr. Lewis Griffin
  • Funded by: UCL Dawes Centre for Future Crime

Felix Soldner

  • PhD student (2018-2021)
  • Topic: Detecting and mitigating hidden fraud and intellectual property crime
  • Supervised with Prof. Shane Johnson
  • Funded by: UCL Dawes Centre for Future Crime

Daniel Hammocks

  • MRes+PhD student (2018 - 2022)
  • Topic: Detecting emerging crimes using data science techniques
  • Supervised with Prof. Kate Bowers
  • Funded by: EPSRC

Isabelle van der Vegt

  • PhD student (2018 - 2021)
  • Website
  • Topic: Understanding and predicting threats of violence using computational linguistics
  • Supervised with Dr Paul Gill (UCL)
  • Funded by Dr P Gill's ERC grant 'GRIEVANCE'

MSc dissertations

  • Thijs Veltman ("Participants's strategies in deception about intentions")
  • Lex Ehrhardt ("Strategies of airport security practitioners to detect suspicious passengers")
  • Wietse Dekker ("Temporal information as cue of deceptive intentions (replication study)"
  • Fleur van der Meer ("Chat-based information elicitation for large-scale deception detection")
  • Isabelle van der Vegt ("Dynamic strategic interviewing to detect deceptive intentions") - now PhD student at UCL
  • Tinde Haarlemmer ("Combined testing to address leakage in the RT Concealed Information Test")
  • Carmen Sergiou ("Detecting informed innocent participants in the RT Concealed Information Test") - now PhD student at Erasmus University Rotterdam

Internships & BSc theses

  • Josh Kamps (cryptocurrency pump-and-dumps; BSc thesis in Computer Science, VU University Amsterdam) - now MSc student at UCL
  • Leonie Waldeck (sampling methods in unbalanced class problems; internship MSc Behavioral Data Science, UvA)
  • Jiri Munich (detecting scientific fraud using computational linguistics; internship Research Master Psychology, UvA)
  • Stella de Ree (verbal deception detection from a linguistic perspective; BSc thesis in Linguistics, UvA)
  • Felix Soldner (detecting fake reviews in an experimental study; internship MSc Cognitive Science, UvA) - now PhD student at UCL
  • Manon Schutte (detecting fake reviews in an experimental study; internship Research Master Psychology, UvA) - now RA at UvA
  • Maximilian Mozes (chat-based information elicitaiton + automated text anonymization; internship) - now at TU Munich, from 4/2019 PhD student at UCL
  • Gaspar Lukacs (concealed information tests with filler items) - now PhD student at University of Vienna

Talks & media

Workshops + invited talks

  • Deception detection!!?? Workshop to security professionals in the programme Predictive Profiling at SoSecure Netherlands (March + October 2018)
  • Crime Science and the crime drop Guest lecture in the VU University Amsterdam module "Biosocial aspects of antisocial behavior" (14 February 2018)
  • What is Crime Science Lecture for the student association of Social Psychology at the University of Amsterdam (December 2017)
  • Why we need a Crime Science Master class at the Extensus honours association of the University of Amsterdam, Free University of Amsterdam, and Amsterdam University College (29 November 2017)
  • Crime Science matters Mini lecture at the 385th anniversary event of the University of Amsterdam (18 November 2017)
  • Terrorism prevention with predictive modeling in R Workshop talk at the N8 event “Building tools and training for crime analysts using R”, University of Manchester (25 September 2017)
  • Practical workshop on deception detection Presented as workshop at the University of Groningen, The Netherlands (13 April 2017, with Yaloe van der Toolen)
  • Modern deception detection Presented as workshop at Labyrint Leiden at the University Leiden, The Netherlands (30 November 2016, with Yaloe van der Toolen)
  • Feit of fictie? (EN: Fact or fiction?) Presented as workshop at Het Nutshuis at the 2016 - Museum Night in The Hague (29 October 2016, with Yaloe van der Toolen)
  • Chat-based information elicitation mini-demonstration/workshop Presented as research demonstration to honours students in Aviation Engineering of the Hogeschool van Amsterdam (Amsterdam, September 2016, with Yaloe van der Toolen & Maximilian Mozes)

Media


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