Assessing computational reproducibility in Behavior Research Methods.

Psychological science has thrived thanks to new methods and innovative practices. Journals, including Behavior Research Methods (BRM), continue to support the dissemination and evaluation of research assets including data, software/hardware, statistical code, and databases of stimuli. However, such research assets rarely allow for computational reproducibility, meaning they are difficult to reuse. Therefore, in this preregistered report, we explore how BRM's authors and BRM structures shape the landscape of functional research assets. Our broad research questions concern: (1) How quickly methods and analytical techniques reported in BRM can be used and developed further by other scientists; (2) Whether functionality has improved following changes to BRM journal policy in support of computational reproducibility; (3) Whether we can disentangle such policy changes from changes in reproducibility over time. We randomly sampled equal numbers of papers (N = 204) published in BRM before and after the implementation of policy changes. Pairs of researchers recorded how long it took to ensure assets (data, software/hardware, statistical code, and materials) were fully operational. They also coded the completeness and reusability of the assets. While improvements were observed in all measures, only changes to completeness were altered significantly following the policy changes (d = .37). The effects varied between different types of research assets, with data sets from surveys/experiments showing the largest improvements in completeness and reusability. Perhaps more importantly, changes to policy do appear to have improved the life span of research products by reducing natural decline. We conclude with a discussion of how, in the future, research and policy might better support computational reproducibility within and beyond psychological science.

The influence of multiple trials and computer-mediated communication on collaborative and individual semantic recall.

Collaborative inhibition is a phenomenon where collaborating groups experience a decrement in recall when interacting with others. Despite this, collaboration has been found to improve subsequent individual recall. We explore these effects in semantic recall, which is seldom studied in collaborative retrieval. We also examine "parallel CMC", a synchronous form of computer-mediated communication that has previously been found to improve collaborative recall [Hinds, J. M., & Payne, S. J. (2016). Collaborative inhibition and semantic recall: Improving collaboration through computer-mediated communication. Applied Cognitive Psychology, 30(4), 554-565]. Sixty three triads completed a semantic recall task, which involved generating words beginning with "PO" or "HE" across three recall trials, in one of three retrieval conditions: Individual-Individual-Individual (III), Face-to-face-Face-to-Face-Individual (FFI) and Parallel-Parallel-Individual (PPI). Collaborative inhibition was present across both collaborative conditions. Individual recall in Recall 3 was higher when participants had previously collaborated in comparison to recalling three times individually. There was no difference between face-to-face and parallel CMC recall, however subsidiary analyses of instance repetitions and subjective organisation highlighted differences in group members' approaches to recall in terms of organisation and attention to others' contributions. We discuss the implications of these findings in relation to retrieval strategy disruption.

Digital data and personality: A systematic review and meta-analysis of human perception and computer prediction.

In recent years, our increasing use of technology has resulted in the production of vast amounts of data. Consequently, many researchers have analyzed digital data in attempt to understand its relationship with individuals' personalities. Such endeavors have inspired efforts from divergent fields, resulting in widely dispersed findings that are seldom synthesized. In this two-part study, we draw from two distinct areas of personality prediction across psychology and computer science to explore the convergent validity of self-reports with human perception and machine learning algorithms, the identifiability of the Big Five traits, and the predictability of different types of data. In Study 1, five meta-analyses of human perception studies integrating findings from 24,124 individuals rated across 30 independent samples demonstrated moderate convergent validity across all traits (ranging from ρ = 0.38 for Neuroticism, to ρ = 0.57 for Openness). In Study 2, a multilevel meta-analysis of computer prediction studies reporting 534 effect sizes across 42 studies also demonstrated moderate convergent validity (ρ = 0.30). Multivariate analyses of the significant moderators highlighted that X, Facebook, Sina Weibo, videos, and smartphones had a negative impact on the variance identified. Finally, in synthesizing the extant literature, we discuss the measures used to assess personality and the analytical approaches adopted. We identify the strengths and limitations across each field and explain how interdisciplinary methodologies could advance the testing and development of psychological theory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Digital traces of offline mobilization.

Since 2009, there has been an increase in global protests and related online activity. Yet, it is unclear how and why online activity is related to the mobilization of offline collective action. One proposition is that online polarization (or a relative change in intensity of posting mobilizing content around a salient grievance) can mobilize people offline. The identity-norm nexus and normative alignment models of collective action further argue that to be mobilizing, these posts need to be socially validated. To test these propositions, across two analyses, we used digital traces of online behavior and data science techniques to model people's online and offline behavior around a mass protest. In Study 1a, we used Twitter behavior posted on the day of the protest by attendees or nonattendees (759 users; 7,592 tweets) to train and test a classifier that predicted, with 80% accuracy, who participated in offline collective action. Attendees used their mobile devices to plan logistics and broadcast their presence at the protest. In Study 1b, using the longitudinal Twitter data and metadata of a subset of users from Study 1a (209 users; 277,556 tweets), we found that participation in the protest was not associated with an individual's online polarization over the year prior to the protest, but it was positively associated with the validation ("likes") they received on their relevant posts. These two studies demonstrate that rather than being low cost or trivial, socially validated online interactions about a grievance are actually key to the mobilization and enactment of collective action. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Personality and Motives for Social Media Use When Physically Distanced: A Uses and Gratifications Approach.

This paper explores individuals' motives for using social media when living under 'social distancing' conditions imposed during the COVID-19 pandemic, where they were instructed to physically distance from other people. Adopting a 'uses and gratifications' approach, and using a previously established five-factor scale, we examine the relationship between individuals' motives for using social media and their personality traits. Hundred and eighty-nine social media users living in the United Kingdom completed surveys assessing their motives for using social media and their personality. Our findings demonstrate that participants were generally motivated to use social media to 'pass time' and to 'maintain relationships.' Further, we find that those high in extraversion in particular use social media to 'maintain relationships.' By comparing our findings to previous studies where face-to-face interaction was not restricted, our findings indicate that individuals' motives for using social media change when they are placed under physical distancing restrictions. We reflect on the potential application of our findings for others experiencing similar conditions, such as those working in remote locations, as well as the potential implications for living in a post-pandemic world with increased virtual 'meetings' using social media.

Exploring Workers' Subjective Experiences of Habit Formation in Cybersecurity: A Qualitative Survey.

Employee behaviors remain at the center of the cybersecurity of workplaces, despite the challenges they face in doing so. Time pressures and competing demands mean that users tend to rely on habitual behaviors that often run counter to good cybersecurity practice. One possible solution may be to encourage positive habit formation. Designing such interventions, however, relies on knowledge of the perception and experience of habit formation in the context of cybersecurity. To this end, a qualitative survey containing open-ended questions was completed by 195 participants (mean age = 35.51, 53 percent female) recruited via an online participant panel. Participants were asked what cybersecurity behaviors they perform at work and how they believe any habits were prompted, formed, and maintained. Thematic analysis identified three over-arching themes: (a) forming habits unavoidably or unconsciously (some were mandated, or formed without conscious awareness), (b) consciously cultivating habits (including the roles of intrinsic motivation and external prompts), and (c) social and organizational influences (including the influence of occupational culture, social modeling, previous experiences, and information gathering practices). Based on these findings, we present guidelines for supporting workplace cybersecurity habit formation reflecting these subjective experiences, namely introducing automatic solutions, facilitating external cues, fostering interest in cybersecurity issues among employees, creating a positive cybersecurity occupational culture and highlighting positive behavior, and providing access to accessible cybersecurity information to employees. These results constitute a first step in identifying how habits can be exploited for positive cybersecurity behavior change in a way that accounts for the reliance on habitual behaviors in busy, time-pressured workplaces.

The evolution of online ideological communities.

Online communities are virtual spaces for users to share interests, support others, and to exchange knowledge and information. Understanding user behavior is valuable to organizations and has applications from marketing to security, for instance, identifying leaders within a community or predicting future behavior. In the present research, we seek to understand the various roles that users adopt in online communities-for instance, who leads the conversation? Who are the supporters? We examine user role changes over time and the pathways that users follow. This allows us to explore the differences between users who progress to leadership positions and users who fail to develop influence. We also reflect on how user role proportions impact the overall health of the community. Here, we examine two online ideological communities, RevLeft and Islamic Awakening (N = 1631; N = 849), and provide a novel approach to identify various types of users. Finally, we study user role trajectories over time and identify community "leaders" from meta-data alone. Study One examined both communities using K-MEANS cluster analysis of behavioral meta-data, which revealed seven user roles. We then mapped these roles against Preece and Schneiderman's (2009) Reader-to-Leader Framework (RtLF). Both communities aligned with the RtLF, where most users were "contributors", many were "collaborators", and few were "leaders". Study Two looked at one community over a two-year period and found that, despite a high churn rate of users, roles were stable over time. We built a model of user role transitions over the two years. This can be used to predict user role changes in the future, which will have implications for community managers and security focused contexts (e.g., analyzing behavioral meta-data from forums and websites known to be associated with illicit activity).

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.