Article
Steven Watson
Risk, benefits, and the affect heuristic in security behaviours
Article
|
6 min read
Report
Fiona Gabbert, Gordon Wright
Quantifying The Effectiveness Of A Rapport-Building Training Programme
Report
|
2 min read
Report
Ewout Meijer, Katherine Hoogesteyn, Brianna Verigin, Danielle Finnick
Rapport Building: Online Vs In-Person Interviews
Report
|
3 min read
Article
Emma Williams, Emma Slade
What Influences Consumer Adoption and Secure Use of Smart Home Technology?
Article
|
3 min read
Article
Oli Buckley
CLICKA
Article
|
4 min read
Article
Kristoffer Geyer
Understanding digital traces
Article
|
4 min read
Article
Duncan Hodges
A to Z Of Data
Article
|
3 min read
Article
Paul Taylor
From data to datum: What should I do in this case?
Article
|
4 min read
Article
Joanne Hinds
Behaviour Prediction: The Challenges and Opportunities of Big Data
Article
|
4 min read
Article
Ryan Boyd, Paul Kapoor
Psychological Profiling and Event Forecasting Using Computational Language Analysis
Article
|
5 min read
Article
Debi Ashenden
Data and the Social and Behavioural Sciences
Article
|
4 min read
Article
Jan-Willem Bullée
Social Engineering: From Thoughts to Awareness
Article
|
3 min read
Article
Heather Shaw, David Ellis
Apple or Android? What your choice of operating system says about you
Article
|
2 min read
Article
David Ellis, Lukasz Piwek
The future of wearable technology
Article
|
2 min read
Article
Paul Taylor
The promise of social science
Article
|
3 min read
Article
Sheryl Prentice
How Technology Could Help Predict Terrorist Attacks
Article
|
3 min read
CLICKA: Collecting and leveraging identity cues with keystroke dynamics

The way in which IT systems are usually secured is through the use of username and password pairs. However, these credentials are all too easily lost, stolen or compromised. The use of behavioural biometrics can be used to supplement these credentials to provide a greater level of assurance in the identity of an authenticated user. However, user behaviours can also be used to ascertain other identifiable information about an individual. In this paper we build upon the notion of keystroke dynamics (the analysis of typing behaviours) to infer an anonymous user’s name and predict their native language. This work found that there is a discernible difference in the ranking of bigrams (based on their timing) contained within the name of a user and those that are not. As a result we propose that individuals will reliably type information they are familiar with in a discernibly different way. In our study we found that it should be possible to identify approximately a third of the bigrams forming an anonymous users name purely from how (not what) they type.

https://doi.org/10.1016/j.cose.2022.102780
An Inventory of Problems–29 (IOP–29) study investigating feigned schizophrenia and random responding in a British community sample

Compared to other Western countries, malingering research is still relatively scarce in the United Kingdom, partly because only a few brief and easy-to-use symptom validity tests (SVTs) have been validated for use with British test-takers. This online study examined the validity of the Inventory of Problems–29 (IOP–29) in detecting feigned schizophrenia and random responding in 151 British volunteers. Each participant took three IOP–29 test administrations: (a) responding honestly; (b) pretending to suffer from schizophrenia; and (c) responding at random. Additionally, they also responded to a schizotypy measure (O-LIFE) under standard instruction. The IOP–29’s feigning scale (FDS) showed excellent validity in discriminating honest responding from feigned schizophrenia (AUC = .99), and its classification accuracy was not significantly affected by the presence of schizotypal traits. Additionally, a recently introduced IOP–29 scale aimed at detecting random responding (RRS) demonstrated very promising results.

(From the journal abstract)


Winters, C. L., Giromini, L., Crawford, T. J., Ales, F., Viglione, D. J., & Warmelink, L. (2020). An Inventory of Problems–29 (IOP–29) study investigating feigned schizophrenia and random responding in a British community sample. Psychiatry, Psychology and Law, 1–20.

https://doi.org/10.1080/13218719.2020.1767720
Behavioral consistency in the digital age

Efforts to infer personality from digital footprints have focused on behavioral stability at the trait level without considering situational dependency. We repeat Shoda, Mischel, and Wright’s (1994) classic study of intraindividual consistency with data on 28,692 days of smartphone usage by 780 people. Using per app measures of ‘pickup’ frequency and usage duration, we found that profiles of daily smartphone usage were significantly more consistent when taken from the same user than from different users (d > 1.46). Random forest models trained on 6 days of behavior identified each of the 780 users in test data with 35.8% / 38.5% (pickup / duration) accuracy. This increased to 73.5% / 75.3% when success was taken as the user appearing in the top 10 predictions (i.e., top 1%). Thus, situation-dependent stability in behavior is present in our digital lives and its uniqueness provides both opportunities and risks to privacy.

(From the journal abstract)


Shaw, H., Taylor, P., Ellis, D. A., & Conchie, S. (2021). Behavioral consistency in the digital age [Preprint]. PsyArXiv.

https://doi.org/10.31234/osf.io/r5wtn
Quantifying smartphone “use”: Choice of measurement impacts relationships between “usage” and health

Problematic smartphone scales and duration estimates of use dominate research that considers the impact of smartphones on people and society. However, issues with conceptualization and subsequent measurement can obscure genuine associations between technology use and health. Here, we consider whether different ways of measuring “smartphone use,” notably through problematic smartphone use (PSU) scales, subjective estimates, or objective logs, lead to contrasting associations between mental and physical health. Across two samples including iPhone (n = 199) and Android (n = 46) users, we observed that measuring smartphone interactions with PSU scales produced larger associations between mental health when compared with subjective estimates or objective logs. Notably, the size of the relationship was fourfold in Study 1, and almost three times as large in Study 2, when relying on a PSU scale that measured smartphone “addiction” instead of objective use. Further, in regression models, only smartphone “addiction” scores predicted mental health outcomes, whereas objective logs or estimates were not significant predictors. We conclude that addressing people’s appraisals including worries about their technology usage is likely to have greater mental health benefits than reducing their overall smartphone use. Reducing general smartphone use should therefore not be a priority for public health interventions at this time.

(From the journal abstract)


Shaw, H., Ellis, D. A., Geyer, K., Davidson, B. I., Ziegler, F. V., & Smith, A. (2020). Quantifying smartphone “use”: Choice of measurement impacts relationships between “usage” and health. Technology, Mind, and Behavior, 1(2).

https://doi.org/10.1037/tmb0000022
The Rise of Consumer Health Wearables: Promises and Barriers

Will consumer wearable technology ever be adopted or accepted by the medical community? Patients and practitioners regularly use digital technology (e.g., thermometers and glucose monitors) to identify and discuss symptoms. In addition, a third of general practitioners in the United Kingdom report that patients arrive with suggestions for treatment based on online search results. However, consumer health wearables are predicted to become the next “Dr Google.” One in six (15%) consumers in the United States currently uses wearable technology, including smartwatches or fitness bands. While 19 million fitness devices are likely to be sold this year, that number is predicted to grow to 110 million in 2018. As the line between consumer health wearables and medical devices begins to blur, it is now possible for a single wearable device to monitor a range of medical risk factors. Potentially, these devices could give patients direct access to personal analytics that can contribute to their health, facilitate preventive care, and aid in the management of ongoing illness. However, how this new wearable technology might best serve medicine remains unclear.

(From the journal abstract)


Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016). The Rise of Consumer Health Wearables: Promises and Barriers. PLOS Medicine, 13(2), e1001953.

https://doi.org/10.1371/journal.pmed.1001953
Understanding the Psychological Process of Avoidance-Based Self-Regulation on Facebook

In relation to social network sites, prior research has evidenced behaviors (e.g., censoring) enacted by individuals used to avoid projecting an undesired image to their online audiences. However, no work directly examines the psychological process underpinning such behavior. Drawing upon the theory of self-focused attention and related literature, a model is proposed to fill this research gap. Two studies examine the process whereby public self-awareness (stimulated by engaging with Facebook) leads to a self-comparison with audience expectations and, if discrepant, an increase in social anxiety, which results in the intention to perform avoidance-based self-regulation. By finding support for this process, this research contributes an extended understanding of the psychological factors leading to avoidance-based regulation when online selves are subject to surveillance.

(From the journal abstract)


Marder, B., Houghton, D., Joinson, A., Shankar, A., & Bull, E. (2016). Understanding the Psychological Process of Avoidance-Based Self-Regulation on Facebook. Cyberpsychology, Behavior, and Social Networking, 19(5), 321–327.

https://doi.org/10.1089/cyber.2015.0564
An evidence synthesis of strategies, enablers and barriers for keeping secrets online regarding the procurement and supply of illicit drugs

This systematic review attempts to understand how people keep secrets online, and in particular how people use the internet when engaging in covert behaviours and activities regarding the procurement and supply of illicit drugs. With the Internet and social media being part of everyday life for most people in western and non-western countries, there are ever-growing opportunities for individuals to engage in covert behaviours and activities online that may be considered illegal or unethical. A search strategy using Medical Subject Headings terms and relevant key words was developed. A comprehensive literature search of published and unpublished studies in electronic databases was conducted. Additional studies were identified from reference lists of previous studies and (systematic) reviews that had similar objectives as this search, and were included if they fulfilled our inclusion criteria. Two researchers independently screened abstracts and full-texts for study eligibility and evaluated the quality of included studies. Disagreements were resolved by a consensus procedure. The systematic review includes 33 qualitative studies and one cross-sectional study, published between 2006 and 2018. Five covert behaviours were identified: the use of communication channels; anonymity; visibility reduction; limited posts in public; following forum rules and recommendations. The same technologies that provide individuals with easy access to information, such as social networking sites and forums, digital devices, digital tools and services, also increase the prevalence of inaccurate information, loss of privacy, identity theft and disinhibited communication. This review takes a rigorous interdisciplinary approach to synthesising knowledge on the strategies adopted by people in keeping secrets online. Whilst the focus is on the procurement and supply of illicit drugs, this knowledge is transferrable to a range of contexts where people keep secrets online. It has particular significance for those who design online/social media applications, and for law enforcement and security agencies.

(From the journal abstract)


Grimani, A., Gavine, A., & Moncur, W. (2020a). An evidence synthesis of strategies, enablers and barriers for keeping secrets online regarding the procurement and supply of illicit drugs. International Journal of Drug Policy, 75, 102621.

https://doi.org/10.1016/j.drugpo.2019.102621
A simple location-tracking app for psychological research

Location data gathered from a variety of sources are particularly valuable when it comes to understanding individuals and groups. However, much of this work has relied on participants’ active engagement in regularly reporting their location. More recently, smartphones have been used to assist with this process, but although commercial smartphone applications are available, these are often expensive and are not designed with researchers in mind. To overcome these and other related issues, we have developed a freely available Android application that logs location accurately, stores the data securely, and ensures that participants can provide consent or withdraw from a study at any time. Further recommendations and R code are provided in order to assist with subsequent data analysis.

(From the journal abstract)


Geyer, K., Ellis, D. A., & Piwek, L. (2019). A simple location-tracking app for psychological research. Behavior Research Methods, 51(6), 2840–2846.

https://doi.org/10.3758/s13428-018-1164-y
Immersive simulations with extreme teams

Extreme teams (ETs) work in challenging, high pressured contexts, where poor performance can have severe consequences. These teams must coordinate their skill sets, align their goals, and develop shared awareness, all under stressful conditions. How best to research these teams poses unique challenges as researchers seek to provide applied recommendations while conducting rigorous research to test how teamwork models work in practice. In this article, we identify immersive simulations as one solution to this, outlining their advantages over existing methodologies and suggesting how researchers can best make use of recent advances in technology and analytical techniques when designing simulation studies. We conclude that immersive simulations are key to ensuring ecological validity and empirically reliable research with ETs.

(From the journal abstract)


Brown, O., Power, N., & Conchie, S. M. (2020). Immersive simulations with extreme teams. Organizational Psychology Review, 10(3–4), 115–135.

https://doi.org/10.1177/2041386620926037
Unraveling the Misconception About Deception and Nervous Behavior

In this article, we attempt to unravel the misconception about deception and nervous behavior. First we will cite research demonstrating that observers believe lie tellers display more nervous behaviors than truth tellers; that observers pay attention to nervous behaviors when they attempt to detect deception; and that lie tellers actually feel more nervous than truth tellers. This is all in alignment with a lie detection approach based on spotting nervous behaviors. We then will argue that the next, vital, step is missing: Research has found that lie tellers generally do not display more than truth tellers the nervous behaviors laypersons and professionals appear to focus on. If observers pay attention to nervous behaviors but lie tellers do not come across as being nervous, lie detection performance is expected to be poor. Research has supported this claim. We finally discuss ideas for research into lie detection based on non-verbal behaviors.

(From the journal abstract)


Vrij, A., & Fisher, R. P. (2020). Unraveling the Misconception About Deception and Nervous Behavior. Frontiers in Psychology, 11, 1377.

https://doi.org/10.3389/fpsyg.2020.01377
What demographic attributes do our digital footprints reveal? A systematic review

To what extent does our online activity reveal who we are? Recent research has demonstrated that the digital traces left by individuals as they browse and interact with others online may reveal who they are and what their interests may be. In the present paper we report a systematic review that synthesises current evidence on predicting demographic attributes from online digital traces. Studies were included if they met the following criteria: (i) they reported findings where at least one demographic attribute was predicted/inferred from at least one form of digital footprint, (ii) the method of prediction was automated, and (iii) the traces were either visible (e.g. tweets) or non-visible (e.g. clickstreams). We identified 327 studies published up until October 2018. Across these articles, 14 demographic attributes were successfully inferred from digital traces; the most studied included gender, age, location, and political orientation. For each of the demographic attributes identified, we provide a database containing the platforms and digital traces examined, sample sizes, accuracy measures and the classification methods applied. Finally, we discuss the main research trends/findings, methodological approaches and recommend directions for future research.

(From the journal abstract)


Hinds, J., & Joinson, A. N. (2018). What demographic attributes do our digital footprints reveal? A systematic review. PLOS ONE, 13(11), e0207112.

https://doi.org/10.1371/journal.pone.0207112
Do smartphone usage scales predict behavior?

Understanding how people use technology remains important, particularly when measuring the impact this might have on individuals and society. However, despite a growing body of resources that can quantify smartphone use, research within psychology and social science overwhelmingly relies on self-reported assessments. These have yet to convincingly demonstrate an ability to predict objective behavior. Here, and for the first time, we compare a variety of smartphone use and ‘addiction’ scales with objective behaviors derived from Apple's Screen Time application. While correlations between psychometric scales and objective behavior are generally poor, single estimates and measures that attempt to frame technology use as habitual rather than ‘addictive’ correlate more favorably with subsequent behavior. We conclude that existing self-report instruments are unlikely to be sensitive enough to accurately predict basic technology use related behaviors. As a result, conclusions regarding the psychological impact of technology are unreliable when relying solely on these measures to quantify typical usage.

(From the journal abstract)


Ellis, D. A., Davidson, B. I., Shaw, H., & Geyer, K. (2019). Do smartphone usage scales predict behavior? International Journal of Human-Computer Studies, 130, 86–92.

https://doi.org/10.1016/j.ijhcs.2019.05.004
How is Extraversion related to Social Media Use? A Literature Review

With nearly 3.5 billion people now using some form of social media, understanding its relationship with personality has become a crucial focus of psychological research.

As such, research linking personality traits to social media behaviour has proliferated in recent years, resulting in a disparate set of literature that is rarely synthesised. To address this, we performed a systematic search that identified 182 studies relating extraversion to social media behaviour.

Our findings highlight that extraversion and social media are studied across six areas: 1) content creation, 2) content reaction, 3) user profile characteristics, 4) patterns of use, 5) perceptions of social media, and 6) aggression, trolling, and excessive use.

We compare these findings to offline behaviour and identify parallels such as extraverts' desire for social attention and their tendency to display positivity. Extraverts are also likely to use social media, spend more time using one or more social media platforms, and regularly create content.

We discuss how this evidence will support the future development and design of social media platforms, and its application across a variety of disciplines such as marketing and human-computer interaction.

(From the journal abstract)


Thomas Bowden-Green, Joanne Hinds & Adam Joinson, 2020. How is extraversion related to social media use? A literature review. Personality and Individual Differences.https://doi.org/10.1016/j.paid.2020.110040

Human and Computer Personality Prediction From Digital Footprints

Is it possible to judge someone accurately from his or her online activity? The Internet provides vast opportunities for individuals to present themselves in different ways, from simple self-enhancement to malicious identity fraud. We often rely on our Internet-based judgments of others to make decisions, such as whom to socialize with, date, or employ. Recently, personality-perception researchers have turned to studying social media and digital devices in order to ask whether a person’s digital traces can reveal aspects of his or her identity. Simultaneously, advances in “big data” analytics have demonstrated that computer algorithms can predict individuals’ traits from their digital traces. In this article, we address three questions: What do we currently know about human- and computer-based personality assessments? How accurate are these assessments? Where are these fields heading? We discuss trends in the current findings, provide an overview of methodological approaches, and recommend directions for future research.

(From the journal abstract)


Joanne Hinds and Adam Joinson. 2019. ‘Human and Computer Personality Prediction From Digital Footprints’. Current Directions in Psychological Science, https://doi.org/10.1177/0963721419827849.

What Demographic Attributes Do Our Digital Footprints Reveal?

To what extent does our online activity reveal who we are? Recent research has demonstrated that the digital traces left by individuals as they browse and interact with others online may reveal who they are and what their interests may be. In the present paper we report a systematic review that synthesises current evidence on predicting demographic attributes from online digital traces. Studies were included if they met the following criteria: (i) they reported findings where at least one demographic attribute was predicted/inferred from at least one form of digital footprint, (ii) the method of prediction was automated, and (iii) the traces were either visible (e.g. tweets) or non-visible (e.g. clickstreams). We identified 327 studies published up until October 2018. Across these articles, 14 demographic attributes were successfully inferred from digital traces; the most studied included gender, age, location, and political orientation. For each of the demographic attributes identified, we provide a database containing the platforms and digital traces examined, sample sizes, accuracy measures and the classification methods applied. Finally, we discuss the main research trends/findings, methodological approaches and recommend directions for future research.

(From the journal abstract)


Joanne Hinds and Adam N. Joinson. 2018. ‘What Demographic Attributes Do Our Digital Footprints Reveal? A Systematic Review’. PLOS ONE, 13 (11): e0207112. https://doi.org/10.1371/journal.pone.0207112.

Can Programming Frameworks Bring Smartphones into the Mainstream of Psychological Science?

Smartphones continue to provide huge potential for psychological science and the advent of novel research frameworks brings new opportunities for researchers who have previously struggled to develop smartphone applications.

However, despite this renewed promise, smartphones have failed to become a standard item within psychological research. Here we consider the key issues that continue to limit smartphone adoption within psychological science and how these barriers might be diminishing in light of ResearchKit and other recent methodological developments.

We conclude that while these programming frameworks are certainly a step in the right direction it remains challenging to create usable research-orientated applications with current frameworks.

Smartphones may only become an asset for psychology and social science as a whole when development software that is both easy to use and secure becomes freely available.

(From the journal abstract)


Piwek, Lukasz, David A. Ellis, and Sally Andrews. 2016. ‘Can Programming Frameworks Bring Smartphones into the Mainstream of Psychological Science?’ Frontiers in Psychology 7. https://doi. org/10.3389/fpsyg.2016.01252.

Mimicry in Online Conversations: An Exploratory Study of Linguistic Analysis Techniques

A number of computational techniques have been proposed that aim to detect mimicry in online conversations. In this paper, we investigate how well these reflect the prevailing cognitive science model, i.e. the Interactive Alignment Model. We evaluate Local Linguistic Alignment, word vectors, and Language Style Matching and show that these measures tend to show the features we expect to see in the IAM, but significantly fall short of the work of human classifiers on the same data set. This reflects the need for substantial additional research on computational techniques to detect mimicry in online conversations. We suggest further work needed to measure these techniques and others more accurately.

(From the journal abstract)


Carrick, Tom, Awais Rashid, and Paul. J. Taylor. 2016. ‘Mimicry in Online Conversations: An Exploratory Study of Linguistic Analysis Techniques’. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 732–36. http://eprints.lancs.ac.uk/80520/1/asonam_mimicry.pdf

Predicting Collective Action from Micro-Blog Data

Global and national events in recent years have shown that social media, and particularly micro-blogging services such as Twitter, can be a force for good (e.g., Arab Spring) and harm (e.g., London riots). In both of these examples, social media played a key role in group formation and organisation, and in the coordination of the group’s subsequent collective actions (i.e., the move from rhetoric to action).

Surprisingly, despite its clear importance, little is understood about the factors that lead to this kind of group development and the transition to collective action. This paper focuses on an approach to the analysis of data from social media to detect weak signals, i.e., indicators that initially appear at the fringes, but are, in fact, early indicators of such large-scale real-world phenomena.

Our approach is in contrast to existing research which focuses on analysing major themes, i.e., the strong signals, prevalent in a social network at a particular point in time. Analysis of weak signals can provide interesting possibilities for forecasting, with online user-generated content being used to identify and anticipate possible offline future events. We demonstrate our approach through analysis of tweets collected during the London riots in 2011 and use of our weak signals to predict tipping points in that context.

(From the journal abstract)


Charitonidis, Christos, Awais Rashid, and Paul J. Taylor. 2017. ‘Predicting Collective Action from Micro-Blog Data’. In Prediction and Inference from Social Networks and Social Media, 141–70. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-51049-1_7.

Back to top