Facebook as a research tool for the social sciences: opportunities, challenges, ethical considerations, and practical guidelines

Facebook is rapidly gaining recognition as a powerful research tool for the social sciences. It constitutes a large and diverse pool of participants, who can be selectively recruited for both online and offline studies. Additionally, it facilitates data collection by storing detailed records of its users’ demographic profiles, social interactions, and behaviors. With participants’ consent, these data can be recorded retrospectively in a convenient, accurate, and inexpensive way. Based on our experience in designing, implementing, and maintaining multiple Facebook-based psychological studies that attracted over 10 million participants, we demonstrate how to recruit participants using Facebook, incentivize them effectively, and maximize their engagement. We also outline the most important opportunities and challenges associated with using Facebook for research, provide several practical guidelines on how to successfully implement studies on Facebook, and finally, discuss ethical considerations. (PsycINFO Database Record (c) 2015 APA, all rights reserved

Quality control in public participation assessments of water quality: the OPAL Water Survey

BACKGROUND: Public participation in scientific data collection is a rapidly expanding field. In water quality surveys, the involvement of the public, usually as trained volunteers, generally includes the identification of aquatic invertebrates to a broad taxonomic level. However, quality assurance is often not addressed and remains a key concern for the acceptance of publicly-generated water quality data. The Open Air Laboratories (OPAL) Water Survey, launched in May 2010, aimed to encourage interest and participation in water science by developing a 'low-barrier-to-entry' water quality survey. During 2010, over 3000 participant-selected lakes and ponds were surveyed making this the largest public participation lake and pond survey undertaken to date in the UK. But the OPAL approach of using untrained volunteers and largely anonymous data submission exacerbates quality control concerns. A number of approaches were used in order to address data quality issues including: sensitivity analysis to determine differences due to operator, sampling effort and duration; direct comparisons of identification between participants and experienced scientists; the use of a self-assessment identification quiz; the use of multiple participant surveys to assess data variability at single sites over short periods of time; comparison of survey techniques with other measurement variables and with other metrics generally considered more accurate. These quality control approaches were then used to screen the OPAL Water Survey data to generate a more robust dataset. RESULTS: The OPAL Water Survey results provide a regional and national assessment of water quality as well as a first national picture of water clarity (as suspended solids concentrations). Less than 10 % of lakes and ponds surveyed were ‘poor’ quality while 26.8 % were in the highest water quality band. CONCLUSIONS: It is likely that there will always be a question mark over untrained volunteer generated data simply because quality assurance is uncertain, regardless of any post hoc data analyses. Quality control at all stages, from survey design, identification tests, data submission and interpretation can all increase confidence such that useful data can be generated by public participants

Fear and deprivation in Trump’s America: A regional analysis of voting behavior in the 2016 and 2020 U.S. presidential elections

Since Trump was elected U.S. President in 2016, researchers have sought to explain his support, with some focusing on structural factors (e.g., economics) and others focusing on psychological factors (e.g., negative emotions). We integrate these perspectives in a regional analysis of 18+ structural variables capturing economic, demographic, and health factors as well as the aggregated neuroticism scores of 3+ million individuals. Results revealed that regions that voted for Trump in 2016 and 2020 had high levels of neuroticism and economic deprivation. Regions that voted for Trump also had high anti-Black implicit bias and low ethnic diversity, though Trump made gains in ethnically diverse regions in 2020. Trump’s voter base differed from the voter base of more traditional Republican candidates and Democrat Bernie Sanders. In sum, structural and psychological factors both explain Trump’s unique authoritarian appeal

Variation in Decision Making

publication-status: PublishedVariation in how organisms allocate their behavior over their lifetimes is key to determining Darwinian fitness, and thus the evolution of human and non-human decision making. In this chapter, we explore how decision making varies across biologically and societally significant scales and what role such variation plays when trying to understand decision making from an evolutionary perspective. In the process, we highlight the importance of explicitly considering variation both when attempting to predict economically and socially important patterns of behavior, and to obtain a deeper understanding of the fundamental biological processes involved. We conclude by identifying key elements of a framework for incorporating variation into a general theory of Darwinian decision making

Injury Risk Estimation Expertise Assessing the ACL Injury Risk Estimation Quiz

Background: Available methods for screening anterior cruciate ligament (ACL) injury risk are effective but limited in application as they generally rely on expensive and time-consuming biomechanical movement analysis. A potential efficient alternative to biomechanical screening is skilled movement analysis via visual inspection (ie, having experts estimate injury risk factors based on observations of athletes’ movements). Purpose: To develop a brief, valid psychometric assessment of ACL injury risk factor estimation skill: the ACL Injury Risk Estimation Quiz (ACL-IQ). Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: A total of 660 individuals participated in various stages of the study, including athletes, physicians, physical therapists, athletic trainers, exercise science researchers/students, and members of the general public in the United States. The ACL-IQ was fully computerized and made available online (www.ACL-IQ.org). Item sampling/reduction, reliability analysis, cross-validation, and convergent/discriminant validity analysis were conducted to optimize the efficiency and validity of the assessment. Results: Psychometric optimization techniques identified a short (mean time, 2 min 24 s), robust, 5-item assessment with high reliability (test-retest: r = 0.90) and consistent discriminability (average difference of exercise science professionals vs general population: Cohen d = 1.98). Exercise science professionals and general population individuals scored 74% and 53% correct, respectively. Convergent and discriminant validity was demonstrated. Scores on the ACL-IQ were most associated with ACL knowledge and various cue utilities and were least associated with domain-general spatial/decision-making ability, personality, or other demographic variables. Overall, 23% of the total sample (40% exercise science professionals; 6% general population) performed better than or equal to the ACL nomogram. Conclusion: This study presents the results of a systematic approach to assess individual differences in ACL injury risk factor estimation skill; the assessment approach is efficient (ie, it can be completed in\3 min) and psychometrically robust. The results provide evidence that some individuals have the ability to visually estimate ACL injury risk factors more accurately than other instrument-based ACL risk estimation methods (ie, ACL nomogram). The ACL-IQ provides the foundation for assessing the efficacy of observational ACL injury risk factor assessment (ie, does simple skilled visual inspection reduce ACL injuries?). It also provides a representative task environment that can be used to increase our understanding of the perceptual-cognitive mechanisms underlying observational movement analysis and to improve injury risk assessment performance

Differences in trait impulsivity indicate diversification of dog breeds into working and show lines

Impulsiveness describes the inability to inhibit behaviour in the presence of salient cues. Trait-level impulsivity exists on a continuum and individual differences can be adaptive in different contexts. While breed related differences in behavioural tendency in the domestic dog (Canis familiaris) are well established, the phenomenon within lines of a breed which have been selected more recently is not well studied, although it may challenge the popular notion of breed-typical behaviour. We describe differences in impulsivity between and within two dog breeds with working and show lines selected for different levels of impulsivity: Border Collies (herding work) and Labrador Retrievers (gun work). Recent show line selection might have lessened differences in impulsivity between breeds. We tested this hypothesis on a dataset of 1161 individuals assessed using a validated psychometric tool (Dog Impulsivity Assessment Scale - DIAS). Collies were more impulsive on average, consistent with the original purpose of breed selection. Regarding line, working Collies differed from working Labradors, but show lines from the two breeds were not significantly different. Altered or relaxed artificial selection for behavioural traits when appearance rather than behaviour become the primary focus for breeders may reduce average differences in impulsivity between breeds in show lines

Evaluating people's perceptions of trust in a robot in a repeated interactions study

Funding Information: Acknowledgment. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642667 (Safety Enables Cooperation in Uncertain Robotic Environments - SECURE). KD acknowledges funding from the Canada 150 Research Chairs Program. Publisher Copyright: © 2020, Springer Nature Switzerland AG This is a post-peer-review, pre-copyedit version of an article published of 'Rossi A., Dautenhahn K., Koay K.L., Walters M.L., Holthaus P. (2020) Evaluating People’s Perceptions of Trust in a Robot in a Repeated Interactions Study. In: Wagner A.R. et al. (eds) Social Robotics. ICSR 2020. Lecture Notes in Computer Science, vol 12483. Springer, Cham. https://doi.org/10.1007/978-3-030-62056-1_38'Trust has been established to be a key factor in fostering human-robot interactions. However, trust can change overtime according to different factors, including a breach of trust due to a robot’s error. In this exploratory study, we observed people’s interactions with a companion robot in a real house, adapted for human-robot interaction experimentation, over three weeks. The interactions happened in six scenarios in which a robot performed different tasks under two different conditions. Each condition included fourteen tasks performed by the robot, either correctly, or with errors with severe consequences on the first or last day of interaction. At the end of each experimental condition, participants were presented with an emergency scenario to evaluate their trust in the robot. We evaluated participants’ trust in the robot by observing their decision to trust the robot during the emergency scenario, and by collecting their views through questionnaires. We concluded that there is a correlation between the timing of an error with severe consequences performed by the robot and the corresponding loss of trust of the human in the robot. In particular, people’s trust is subjected to the initial mental formation

Actual persuasiveness : Impact of personality, age and gender on message type susceptibility

The authors would like to acknowledge and thank all the volunteers who participated in the experiment and provided helpful comments. The first author is funded by an EPSRC doctoral training grant.Postprin

DADOS-Survey: an open-source application for CHERRIES-compliant Web surveys

BACKGROUND: The Internet has been increasingly utilized in biomedical research. From online searching for literature to data sharing, the Internet has emerged as a primary means of research for many physicians and scientists. As a result, Web-based surveys have been employed as an alternative to traditional, paper-based surveys. We describe DADOS-Survey, an open-source Web-survey application developed at our institution that, to the best of our knowledge, is the first to be compliant with the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). DADOS-Survey was designed with usability as a priority, allowing investigators to design and execute their own studies with minimal technical difficulties in doing so. RESULTS: To date, DADOS-Survey has been successfully implemented in five Institutional Review Board-approved studies conducted by various departments within our academic center. Each of these studies employed a Web-survey design as their primary methodology. Our initial experience indicates that DADOS-Survey has been used with relative ease by each of the investigators and survey recipients. This has been further demonstrated through formal and field usability testing, during which time suggestions for improvement were incorporated into the software design. CONCLUSION: DADOS-Survey has the potential to have an important role in the future direction of Web-survey administration in biomedical research. This CHERRIES-compliant application is tailored to the emerging requirements of quality data collection in medicine

Combining Deep Facial and Ambient Features for First Impression Estimation

14th European Conference on Computer Vision (ECCV) -- OCT 08-16, 2016 -- Amsterdam, NETHERLANDSFirst impressions influence the behavior of people towards a newly encountered person or a human-like agent. Apart from the physical characteristics of the encountered face, the emotional expressions displayed on it, as well as ambient information affect these impressions. In this work, we propose an approach to predict the first impressions people will have for a given video depicting a face within a context. We employ pre-trained Deep Convolutional Neural Networks to extract facial expressions, as well as ambient information. After video modeling, visual features that represent facial expression and scene are combined and fed to a Kernel Extreme Learning Machine regressor. The proposed system is evaluated on the ChaLearn Challenge Dataset on First Impression Recognition, where the classification target is the Big Five personality trait labels for each video. Our system achieved an accuracy of 90.94% on the sequestered test set, 0.36% points below the top system in the competition