Academic Appointments


Administrative Appointments


  • Founding Director, Stanford Social Media Lab (2015 - Present)
  • Faculty Director, Stanford Internet Observatory (2023 - Present)
  • Co-Director, Cyber-Policy Center (2023 - Present)

Program Affiliations


  • Symbolic Systems Program

2023-24 Courses


Stanford Advisees


All Publications


  • Variation in social media sensitivity across people and contexts. Scientific reports Vaid, S. S., Kroencke, L., Roshanaei, M., Talaifar, S., Hancock, J. T., Back, M. D., Gosling, S. D., Ram, N., Harari, G. M. 2024; 14 (1): 6571

    Abstract

    Social media impacts people's wellbeing in different ways, but relatively little is known about why this is the case. Here we introduce the construct of "social media sensitivity" to understand how social media and wellbeing associations differ across people and the contexts in which these platforms are used. In a month-long large-scale intensive longitudinal study (total n = 1632; total number of observations = 120,599), we examined for whom and under which circumstances social media was associated with positive and negative changes in social and affective wellbeing. Applying a combination of frequentist and Bayesian multilevel models, we found a small negative average association between social media use AND subsequent wellbeing, but the associations were heterogenous across people. People with psychologically vulnerable dispositions (e.g., those who were depressed, lonely, not satisfied with life) tended to experience heightened negative social media sensitivity in comparison to people who were not psychologically vulnerable. People also experienced heightened negative social media sensitivity when in certain types of places (e.g., in social places, in nature) and while around certain types of people (e.g., around family members, close ties), as compared to using social media in other contexts. Our results suggest that an understanding of the effects of social media on wellbeing should account for the psychological dispositions of social media users, and the physical and social contexts surrounding their use. We discuss theoretical and practical implications of social media sensitivity for scholars, policymakers, and those in the technology industry.

    View details for DOI 10.1038/s41598-024-55064-y

    View details for PubMedID 38503817

    View details for PubMedCentralID 6534991

  • Building resilience to misinformation in communities of color: Results from two studies of tailored digital media literacy interventions NEW MEDIA & SOCIETY Lee, A. Y., Moore, R. C., Hancock, J. T. 2024
  • When Adolescents' Self-Worth Depends on Their Social Media Feedback: A Longitudinal Investigation With Depressive Symptoms COMMUNICATION RESEARCH Schreurs, L., Lee, A. Y., Liu, X., Hancock, J. T. 2024
  • But is it for us? Rural Chinese elders' perceptions, concerns, and physical preferences regarding social robots NEW MEDIA & SOCIETY Liu, X., Shen, Q., Hancock, J. 2024
  • Generative AI Are More Truth-Biased Than Humans: A Replication and Extension of Core Truth-Default Theory Principles JOURNAL OF LANGUAGE AND SOCIAL PSYCHOLOGY Markowitz, D. M., Hancock, J. T. 2023
  • Developing digital resilience: An educational intervention improves elementary students' response to digital challenges COMPUTERS AND EDUCATION OPEN Lee, A. Y., Hancock, J. T. 2023; 5
  • Too tired to connect: Understanding the associations between video-conferencing, social connection and well-being through the lens of zoom fatigue COMPUTERS IN HUMAN BEHAVIOR Queiroz, A. M., Lee, A. Y., Luo, M., Fauville, G., Hancock, J. T., Bailenson, J. N. 2023; 149
  • Quantifying the Systematic Bias in the Accessibility and Inaccessibility of Web Scraping Content From URL-Logged Web-Browsing Digital Trace Data SOCIAL SCIENCE COMPUTER REVIEW Dahlke, R., Kumar, D., Durumeric, Z., Hancock, J. T. 2023
  • From 65 to 103, Older Adults Experience Virtual Reality Differently Depending on Their Age: Evidence from a Large-Scale Field Study in Nursing Homes and Assisted Living Facilities. Cyberpsychology, behavior and social networking Moore, R. C., Hancock, J. T., Bailenson, J. N. 2023

    Abstract

    There is growing interest in applications of virtual reality (VR) to improve the lives of older adults, but the limited research on older adults and VR largely treats older adults as a monolith, ignoring the substantial differences across 65 to 100+ year olds that may affect their experience of VR. There are also few existing studies examining the experiences and challenges facing those who facilitate VR for older adults (e.g., caregiving staff). We address these limitations through two studies. In study 1, we explore variation within older adults' experiences with VR through a field study of VR use among a large (N = 245) and age-diverse (Mage = 83.6 years, SDage = 7.9, range = 65-103 years) sample of nursing home and assisted living facility residents across 10 U.S. states. Age was negatively associated with the extent to which older adults enjoyed VR experiences. However, the negative relationship between age and older adults' attitudes toward VR was significantly less negative than the relationship between age and their attitudes toward other technologies (cell phones and voice assistants). In study 2, we surveyed caregiving staff (N = 39) who facilitated the VR experiences for older adult residents and found that the caregiving staff generally enjoyed the activity relative to other activities and felt it to be beneficial to their relationship with residents.

    View details for DOI 10.1089/cyber.2023.0188

    View details for PubMedID 38011717

  • Social media mindsets: a new approach to understanding social media use and psychological well-being JOURNAL OF COMPUTER-MEDIATED COMMUNICATION Lee, A. Y., Hancock, J. T. 2023; 29 (1)
  • Publisher Correction: Artificial intelligence in communication impacts language and social relationships. Scientific reports Hohenstein, J., Kizilcec, R. F., DiFranzo, D., Aghajari, Z., Mieczkowski, H., Levy, K., Naaman, M., Hancock, J., Jung, M. F. 2023; 13 (1): 16616

    View details for DOI 10.1038/s41598-023-43601-0

    View details for PubMedID 37789137

    View details for PubMedCentralID PMC10547796

  • Linguistic Markers of Inherently False AI Communication and Intentionally False Human Communication: Evidence From Hotel Reviews JOURNAL OF LANGUAGE AND SOCIAL PSYCHOLOGY Markowitz, D. M., Hancock, J. T., Bailenson, J. N. 2023
  • Black representation in social media well-being research: A scoping review of social media experience and psychological well-being among Black users in the United States NEW MEDIA & SOCIETY Park, J., Hallman, J., Liu, X., Hancock, J. 2023
  • Exposure to untrustworthy websites in the 2020 US election. Nature human behaviour Moore, R. C., Dahlke, R., Hancock, J. T. 2023

    Abstract

    Research using large-scale data on individuals' internet use has provided vital information about the scope and nature of exposure to misinformation online. However, most prior work relies on data collected during the 2016 US election. Here we examine exposure to untrustworthy websites during the 2020 US election, using over 7.5 million website visits from 1,151 American adults. We find that 26.2% (95% confidence interval 22.5% to 29.8%) of Americans were exposed to untrustworthy websites in 2020, down from 44.3% (95% confidence interval 40.8% to 47.7%) in 2016. Older adults and conservatives continued to be the most exposed in 2020 as in 2016, albeit at lower rates. The role of online platforms in exposing people to untrustworthy websites changed, with Facebook playing a smaller role in 2020 than in 2016. Our findings do not minimize misinformation as a key social problem, but instead highlight important changes in its consumption, suggesting directions for future research and practice.

    View details for DOI 10.1038/s41562-023-01564-2

    View details for PubMedID 37055575

    View details for PubMedCentralID 7124954

  • Artificial intelligence in communication impacts language and social relationships. Scientific reports Hohenstein, J., Kizilcec, R. F., DiFranzo, D., Aghajari, Z., Mieczkowski, H., Levy, K., Naaman, M., Hancock, J., Jung, M. F. 2023; 13 (1): 5487

    Abstract

    Artificial intelligence (AI) is already widely used in daily communication, but despite concerns about AI's negative effects on society the social consequences of using it to communicate remain largely unexplored. We investigate the social consequences of one of the most pervasive AI applications, algorithmic response suggestions ("smart replies"), which are used to send billions of messages each day. Two randomized experiments provide evidence that these types of algorithmic recommender systems change how people interact with and perceive one another in both pro-social and anti-social ways. We find that using algorithmic responses changes language and social relationships. More specifically, it increases communication speed, use of positive emotional language, and conversation partners evaluate each other as closer and more cooperative. However, consistent with common assumptions about the adverse effects of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase the speed of communication and improve interpersonal perceptions, the prevailing anti-social connotations of AI undermine these potential benefits if used overtly.

    View details for DOI 10.1038/s41598-023-30938-9

    View details for PubMedID 37015964

    View details for PubMedCentralID 3783449

  • To use or be used? The role of agency in social media use and well-being FRONTIERS IN COMPUTER SCIENCE Lee, A. Y., Ellison, N. B., Hancock, J. T. 2023; 5
  • Human heuristics for AI-generated language are flawed. Proceedings of the National Academy of Sciences of the United States of America Jakesch, M., Hancock, J. T., Naaman, M. 2023; 120 (11): e2208839120

    Abstract

    Human communication is increasingly intermixed with language generated by AI. Across chat, email, and social media, AI systems suggest words, complete sentences, or produce entire conversations. AI-generated language is often not identified as such but presented as language written by humans, raising concerns about novel forms of deception and manipulation. Here, we study how humans discern whether verbal self-presentations, one of the most personal and consequential forms of language, were generated by AI. In six experiments, participants (N = 4,600) were unable to detect self-presentations generated by state-of-the-art AI language models in professional, hospitality, and dating contexts. A computational analysis of language features shows that human judgments of AI-generated language are hindered by intuitive but flawed heuristics such as associating first-person pronouns, use of contractions, or family topics with human-written language. We experimentally demonstrate that these heuristics make human judgment of AI-generated language predictable and manipulable, allowing AI systems to produce text perceived as "more human than human." We discuss solutions, such as AI accents, to reduce the deceptive potential of language generated by AI, limiting the subversion of human intuition.

    View details for DOI 10.1073/pnas.2208839120

    View details for PubMedID 36881628

  • Video-conferencing usage dynamics and nonverbal mechanisms exacerbate Zoom Fatigue, particularly for women COMPUTERS IN HUMAN BEHAVIOR REPORTS Fauville, G., Luo, M., Queiroz, A. M., Lee, A., Bailenson, J. N., Hancock, J. 2023; 10
  • Contextual considerations for deception production and detection in forensic interviews. Frontiers in psychology Markowitz, D. M., Hancock, J. T., Woodworth, M. T., Ely, M. 2023; 14: 1134052

    Abstract

    Most deception scholars agree that deception production and deception detection effects often display mixed results across settings. For example, some liars use more emotion than truth-tellers when discussing fake opinions on abortion, but not when communicating fake distress. Similarly, verbal and nonverbal cues are often inconsistent predictors to assist in deception detection, leading to mixed accuracies and detection rates. Why are lie production and detection effects typically inconsistent? In this piece, we argue that aspects of the context are often unconsidered in how lies are produced and detected. Greater theory-building related to contextual constraints of deception are therefore required. We reintroduce and extend the Contextual Organization of Language and Deception (COLD) model, a framework that outlines how psychological dynamics, pragmatic goals, and genre conventions are aspects of the context that moderate the relationship between deception and communication behavior such as language. We extend this foundation by proposing three additional aspects of the context - individual differences, situational opportunities for deception, and interpersonal characteristics - for the COLD model that can specifically inform and potentially improve forensic interviewing. We conclude with a forward-looking perspective for deception researchers and practitioners related to the need for more theoretical explication of deception and its detection related to the context.

    View details for DOI 10.3389/fpsyg.2023.1134052

    View details for PubMedID 36824303

    View details for PubMedCentralID PMC9941173

  • People, places, and time: a large-scale, longitudinal study of transformed avatars and environmental context in group interaction in the metaverse JOURNAL OF COMPUTER-MEDIATED COMMUNICATION Han, E., Miller, M. R., DeVeaux, C., Jun, H., Nowak, K. L., Hancock, J. T., Ram, N., Bailenson, J. N. 2023; 28 (2)
  • Descriptive Linguistic Patterns of Group Conversations in VR DeVeaux, C., Markowitz, D. M., Han, E., Miller, M., Hancock, J. T., Bailenson, J. N., IEEE IEEE COMPUTER SOC. 2023: 785-786
  • A digital media literacy intervention for older adults improves resilience to fake news. Scientific reports Moore, R. C., Hancock, J. T. 2022; 12 (1): 6008

    Abstract

    Older adults are especially susceptible to fake news online, possibly because they are less digitally literate compared to younger individuals. Interventions for older adults have emerged to improve digital literacy, although there has been little evaluation of their effectiveness in improving older adults' resilience to fake news. We report the results of a digital literacy intervention for older adults administered during the 2020 U.S.election. The intervention was a 1-hour, self-directed series of interactive modules designed to teach concepts and skills for identifying misinformation online. Consistent with our pre-registered hypothesis, older adults (Mage=67) in the treatment condition (N=143) significantly improved their likelihood ofaccurately discerning fake from true news from 64% pre-intervention to 85%post-intervention. In contrast, older adults in the control condition (N=238) did not significantly improve (from 55% to 57%). The treated older adults were also more likely to employ strategies for identifying misinformation online compared to pre-intervention and the control group.

    View details for DOI 10.1038/s41598-022-08437-0

    View details for PubMedID 35397631

  • Authentic First Impressions Relate to Interpersonal, Social, and Entrepreneurial Success SOCIAL PSYCHOLOGICAL AND PERSONALITY SCIENCE Markowitz, D. M., Kouchaki, M., Gino, F., Hancock, J. T., Boyd, R. L. 2022
  • Folk Theories of Online Dating: Exploring People's Beliefs About the Online Dating Process and Online Dating Algorithms SOCIAL MEDIA + SOCIETY Huang, S., Hancock, J., Tong, S. 2022; 8 (2)
  • Effects of Using Artificial Intelligence on Interpersonal Perceptions of Job Applicants. Cyberpsychology, behavior and social networking Weiss, D., Liu, S. X., Mieczkowski, H., Hancock, J. T. 1800

    Abstract

    Text-based artificial intelligence (AI) systems are increasingly integrated into a host of interpersonal domains. Although decision-making and person perception in hiring and employment opportunities have been an area of psychological interest for many years, only recently have scholars begun to investigate the role that AI plays in this context. To better understand the impact of AI in employment-related contexts, we conducted two experiments investigating how the use of AI by applicants influences their job opportunities. In our preregistered Study 1, we examined how a prospective job applicants' use of AI, as well as their language status (native English speaker or non-native English speaker), influenced participants' impressions of their warmth, competence, social attractiveness, and hiring desirability. In Study 2, we examined how receiving assistance impacted interpersonal perceptions, and how perceptions might change whether the help was provided by AI or by another human. The results from both experiments suggest that the use of AI technologies can negatively influence perceptions of jobseekers. This negative impact may be grounded in the perception of receiving any type of help, whether it be from a machine or a person. These studies provide additional evidence for the Computers as Social Actors framework and advance our understanding of AI-Mediated Communication. The results also raise questions about transparency and deception related to AI use in interpersonal contexts.

    View details for DOI 10.1089/cyber.2020.0863

    View details for PubMedID 35021895

  • The Truth Project JOURNAL OF LANGUAGE AND SOCIAL PSYCHOLOGY Markowitz, D. M., Blackburn, K. G., Saxena, K., Marion, J., Olivarez, O., Hernandez, R., Woodworth, M. T., Hancock, J. T. 2021
  • Will You Go on a Date with Me? Predicting First Dates from Linguistic Traces in Online Dating Messages JOURNAL OF LANGUAGE AND SOCIAL PSYCHOLOGY Huang, S. A., Hancock, J. T. 2021
  • Not All AI are Equal: Exploring the Accessibility of AI-Mediated Communication Technology COMPUTERS IN HUMAN BEHAVIOR Goldenthal, E., Park, J., Liu, S. X., Mieczkowski, H., Hancock, J. T. 2021; 125
  • An Explication of Identity Shift Theory Getting Our Shift Together JOURNAL OF MEDIA PSYCHOLOGY-THEORIES METHODS AND APPLICATIONS Carr, C. T., Kim, Y., Valov, J. J., Rosenbaum, J. E., Johnson, B. K., Hancock, J. T., Gonzales, A. L. 2021; 33 (4): 202-214
  • The Role of Subjective Construals on Reporting and Reasoning about Social Media Use SOCIAL MEDIA + SOCIETY Lee, A. Y., Katz, R., Hancock, J. 2021; 7 (3)
  • Age-Related Differences in Experiences with Social Distancing at the Onset of the COVID-19 Pandemic: A Computational and Content Analytic Investigation of Natural Language. JMIR human factors Moore, R. C., Lee, A. Y., Hancock, J. T., Halley, M. C., Linos, E. 2021

    Abstract

    BACKGROUND: As COVID-19 poses different levels of threat to people of different ages, health communication regarding prevention measures such as social distancing and isolation may be strengthened by understanding the unique experiences of different age groups.OBJECTIVE: The aim was to examine how people of different ages (1) experienced the impact of the COVID-19 pandemic and (2) their respective rates and reasons for compliance or non-compliance with social distancing and isolation health guidance.METHODS: We fielded a survey on social media (N = 17,287) early in the pandemic to examine the emotional impact of COVID-19 and individuals' rates and reasons for non-compliance with public health guidance, using computational and content analytic methods of linguistic analysis. The majority of our participants (76.5%) were from the United States.RESULTS: Younger (18-31), middle-aged (32-44, 45-64), and older (65+) individuals significantly varied in how they described the impact of COVID-19 on their lives, including their emotional experience, self-focused attention, and topical concerns. Younger individuals were more emotionally negative and self-focused, while middle-aged people were other-focused and concerned with family. The oldest and most at-risk group was most concerned with health-related terms but were also lower in anxiety and higher in the use of emotionally positive terms than the other, less at-risk age groups. While all groups discussed topics such as acquiring essential supplies, they differentially experienced the impact of school closures and limited social interactions. We also found relatively high rates of non-compliance with COVID-19 prevention measures, such as social distancing and self-isolation, with younger people being more likely to be non-compliant than older people, (P < .001). Among the 43% of respondents who did not fully comply with health orders, people differed substantially in the reasons they gave for non-compliance. The most common reason for non-compliance was not being able to afford missing work (57.3%). While work obligations proved challenging for participants across ages, younger people struggled more to find adequate space to self-isolate and manage their mental and physical health; middle-aged people faced more concerns regarding childcare; and older people perceived themselves as able to take sufficient precautions.CONCLUSIONS: Analysis of natural language can provide insight into rapidly developing public health challenges like the COVID-19 pandemic, uncovering individual differences in emotional experiences and health-related behaviors. In this case, our analyses revealed significant differences between different age groups in feelings about and responses to public health orders aimed to mitigate the spread of COVID-19. To improve public compliance with health orders as the pandemic continues, health communication strategies could be made more effective by being tailored to these age-related differences.CLINICALTRIAL:

    View details for DOI 10.2196/26043

    View details for PubMedID 33914689

  • The Social Impact of Deepfakes CYBERPSYCHOLOGY BEHAVIOR AND SOCIAL NETWORKING Hancock, J. T., Bailenson, J. N. 2021; 24 (3): 149–52

    View details for DOI 10.1089/cyber.2021.29208.jth

    View details for Web of Science ID 000632152700002

    View details for PubMedID 33760669

  • Identifying Silver Linings During the Pandemic Through Natural Language Processing. Frontiers in psychology Lossio-Ventura, J. A., Lee, A. Y., Hancock, J. T., Linos, N., Linos, E. 2021; 12: 712111

    Abstract

    COVID-19 has presented an unprecedented challenge to human welfare. Indeed, we have witnessed people experiencing a rise of depression, acute stress disorder, and worsening levels of subclinical psychological distress. Finding ways to support individuals' mental health has been particularly difficult during this pandemic. An opportunity for intervention to protect individuals' health & well-being is to identify the existing sources of consolation and hope that have helped people persevere through the early days of the pandemic. In this paper, we identified positive aspects, or "silver linings," that people experienced during the COVID-19 crisis using computational natural language processing methods and qualitative thematic content analysis. These silver linings revealed sources of strength that included finding a sense of community, closeness, gratitude, and a belief that the pandemic may spur positive social change. People's abilities to engage in benefit-finding and leverage protective factors can be bolstered and reinforced by public health policy to improve society's resilience to the distress of this pandemic and potential future health crises.

    View details for DOI 10.3389/fpsyg.2021.712111

    View details for PubMedID 34539512

  • "Bringing you into the zoom": The power of authentic engagement in a time of crisis in the USA JOURNAL OF CHILDREN AND MEDIA Lee, A. Y., Moskowitz-Sweet, G., Pelavin, E., Rivera, O., Hancock, J. T. 2020
  • Assessing Mental Health among College Students Using Mobile Apps: Acceptability and Feasibility JOURNAL OF COLLEGE STUDENT PSYCHOTHERAPY Palesh, O., Oakley-Girvan, I., Richardson, A., Nelson, L. M., Clark, R., Hancock, J., Acle, C., Lavista, J. M., Miller, Y., Gore-Felton, C. 2020
  • Priming Effects of Social Media Use Scales on Well-Being Outcomes: The Influence of Intensity and Addiction Scales on Self-Reported Depression SOCIAL MEDIA + SOCIETY Mieczkowski, H., Lee, A. Y., Hancock, J. T. 2020; 6 (4)
  • The Deception Spiral: Corporate Obfuscation Leads to Perceptions of Immorality and Cheating Behavior JOURNAL OF LANGUAGE AND SOCIAL PSYCHOLOGY Markowitz, D. M., Kouchaki, M., Hancock, J. T., Gino, F. 2020
  • Older Adults, Social Technologies, and the Coronavirus Pandemic: Challenges, Strengths, and Strategies for Support SOCIAL MEDIA + SOCIETY Moore, R. C., Hancock, J. T. 2020; 6 (3)
  • The Outsourcing of Online Dating: Investigating the Lived Experiences of Online Dating Assistants Working in the Contemporary Gig Economy SOCIAL MEDIA + SOCIETY Rochadiat, A. P., Tong, S., Hancock, J. T., Stuart-Ulin, C. 2020; 6 (3)
  • Credibility Perceptions and Detection Accuracy of Fake News Headlines on Social Media: Effects of Truth-Bias and Endorsement Cues COMMUNICATION RESEARCH Luo, M., Hancock, J. T., Markowitz, D. M. 2020
  • The Analysis of Nonverbal Communication: The Dangers of Pseudoscience in Security and Justice Contexts ANUARIO DE PSICOLOGIA JURIDICA Denault, V., Plusquellec, P., Jupe, L. M., St-Yves, M., Dunbar, N. E., Hartwig, M., Sporer, S. L., Rioux-Turcotte, J., Jarry, J., Walsh, D., Otgaar, H., Viziteu, A., Talwar, V., Keatley, D. A., Blandon-Gitlin, I., Townson, C., Deslauriers-Varin, N., Lilienfeld, S. O., Patterson, M. L., Areh, I., Allan, A., Cameron, H., Boivin, R., ten Brinke, L., Masip, J., Bull, R., Cyr, M., Hope, L., Stromwall, L. A., Bennett, S. J., Al Menaiya, F., Leo, R. A., Vredeveldt, A., Laforest, M., Honts, C. R., Manzanero, A. L., Mann, S., Granhag, P., Ask, K., Gabbert, F., Guay, J., Coutant, A., Hancock, J., Manusov, V., Burgoon, J. K., Kleinman, S. M., Wright, G., Landstrom, S., Freckelton, I., Vernham, Z., van Koppen, P. J. 2020; 30 (1): 1–12

    View details for DOI 10.5093/apj2019a9

    View details for Web of Science ID 000509344000001

  • AI-Mediated Communication: Definition, Research Agenda, and Ethical Considerations JOURNAL OF COMPUTER-MEDIATED COMMUNICATION Hancock, J. T., Naaman, M., Levy, K. 2020; 25 (1): 89–100
  • Context in a bottle: Language-action cues in spontaneous computer mediated deception COMPUTERS IN HUMAN BEHAVIOR Ho, S., Hancock, J. T. 2019; 91: 33–41
  • Evaluation of a Mobile Device Survey System for Behavioral Risk Factors (SHAPE): App Development and Usability Study. JMIR formative research Oakley-Girvan, I., Lavista, J. M., Miller, Y., Davis, S., Acle, C., Hancock, J., Nelson, L. M. 2019; 3 (1): e10246

    Abstract

    BACKGROUND: Risk factors, including limited exercise, poor sleep, smoking, and alcohol and drug use, if mitigated early, can improve long-term health. Risk prevalence has traditionally been measured using methods that now have diminished participation rates. With >75% of American citizens owning smartphones, new data collection methods using mobile apps can be evaluated.OBJECTIVE: The objective of our study was to describe the development, implementation, and evaluation of a mobile device-based survey system for behavioral risk assessment. Specifically, we evaluated its feasibility, usability, acceptability, and validity.METHODS: We enrolled 536 students from 3 Vermont State Colleges. Iterative mobile app development incorporated focus groups, extensive testing, and the following 4 app versions: iOS standard, iOS gamified, Android standard, and Android gamified. We aimed to capture survey data, paradata, and ambient data such as geolocation. Using 3 separate surveys, we asked a total of 27 questions that included demographic characteristics, behavioral health, and questions regarding the app's usability and survey process.RESULTS: Planned enrollment was exceeded in just a few days. There were 1392 "hits" to the landing page where the app could be downloaded. Excluding known project testers and others not part of the study population, 670 participants downloadeded the SHAPE app. Of those, 94.9% of participants (636/670) agreed to participate by providing in-app consent. Of the 636 who provided consent, 84.3% (536/636) were deemed eligible for the study. The majority of eligible respondents completed the initial survey (459/536, 85.6%), whereas 29.9% (160/536) completed the second survey and 28.5% (153/536) completed the third survey. The SHAPE survey obtained 414 participants on the behavioral risk items in survey 1, which is nearly double the 209 participants who completed the traditional Vermont College Health Survey in 2014. SHAPE survey responses were consistent with the traditionally collected Vermont College Health Survey data.CONCLUSIONS: This study provides data highlighting the potential for mobile apps to improve population-based health, including an assessment of recruitment methods, burden and response rapidity, and future adaptations. Although gamification and monetary rewards were relatively unimportant to this study population, item response theory may be technologically feasible to reduce individual survey burden. Additional data collected by smartphones, such as geolocation, could be important in additional analysis, such as neighborhood characteristics and their impact on behavioral risk factors. Mobile tools that offer rapid adaptation for specific populations may improve research data collection for primary prevention and could be used to improve engagement and health outcomes.

    View details for PubMedID 30684441

  • Helping Not Hurting: Applying the Stereotype Content Model and BIAS Map to Social Robotics Mieczkowski, H., Liu, S., Hancock, J., Reeves, B., IEEE IEEE. 2019: 222–29
  • Self-disclosure and social media: motivations, mechanisms and psychological well-being. Current opinion in psychology Luo, M. n., Hancock, J. T. 2019; 31: 110–15

    Abstract

    Self-disclosure is pervasive on social media and has significant implications for psychological well-being. In this review we synthesize recent research on the motivations, mechanisms and effects of self-disclosure on well-being and then propose a framework that highlights the bidirectional relationship between self-disclosure and well-being. The framework details the mechanisms by which self-disclosure on social media can influence well-being and how self-disclosure fulfills particular needs of individuals with different well-being characteristics. We call for future research to examine the proposed bi-directional relationship, especially studies designed to tease out causal effects.

    View details for DOI 10.1016/j.copsyc.2019.08.019

    View details for PubMedID 31563107

  • AI-Mediated Communication: How the Perception that Profile Text was Written by AI Affects Trustworthiness Jakesch, M., French, M., Ma, X., Hancock, J. T., Naaman, M., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • Psychological and physiological effects of applying self-control to the mobile phone. PloS one Markowitz, D. M., Hancock, J. T., Bailenson, J. N., Reeves, B. n. 2019; 14 (11): e0224464

    Abstract

    This preregistered study examined the psychological and physiological consequences of exercising self-control with the mobile phone. A total of 125 participants were randomly assigned to sit in an unadorned room for six minutes and either (a) use their mobile phone, (b) sit alone with no phone, or (c) sit with their device but resist using it. Consistent with prior work, participants self-reported more concentration difficulty and more mind wandering with no device present compared to using the phone. Resisting the phone led to greater perceived concentration abilities than sitting without the device (not having external stimulation). Failing to replicate prior work, however, participants without external stimulation did not rate the experience as less enjoyable or more boring than having something to do. We also observed that skin conductance data were consistent across conditions for the first three-minutes of the experiment, after which participants who resisted the phone were less aroused than those who were without the phone. We discuss how the findings contribute to our understanding of exercising self-control with mobile media and how psychological consequences, such as increased mind wandering and focusing challenges, relate to periods of idleness or free thinking.

    View details for DOI 10.1371/journal.pone.0224464

    View details for PubMedID 31682619

  • Lies in the Eye of the Beholder: Asymmetric Beliefs about One's Own and Others' Deceptiveness in Mediated and Face-to-Face Communication COMMUNICATION RESEARCH Toma, C. L., Jiang, L., Hancock, J. T. 2018; 45 (8): 1167–92
  • Deception in Mobile Dating Conversations JOURNAL OF COMMUNICATION Markowitz, D. M., Hancock, J. T. 2018; 68 (3): 547–69

    View details for DOI 10.1093/joc/jqy019

    View details for Web of Science ID 000449503100011

  • Psychopaths Online: The Linguistic Traces of Psychopathy in Email, Text Messaging and Facebook MEDIA AND COMMUNICATION Hancock, J. T., Woodworth, M., Boochever, R. 2018; 6 (3): 83–92
  • Computer-Mediated Deception: Collective Language-Action Cues as Stigmergic Signals for Computational Intelligence Ho, S., Hancock, J. T., Bui, T. X. HICSS. 2018: 1671-1680
  • Fake News in the News: An Analysis of Partisan Coverage of the Fake News Phenomenon Che, X., Metaxa-Kakavouli, D., Hancock, J. T., ACM ASSOC COMPUTING MACHINERY. 2018: 289–92
  • Ethical Dilemma: Deception Dynamics in Computer-Mediated Group Communication JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY Ho, S., Hancock, J. T., Booth, C. 2017; 68 (12): 2729–42

    View details for DOI 10.1002/asi.23849

    View details for Web of Science ID 000414718000004

  • Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data JOURNAL OF MEDICAL INTERNET RESEARCH Kim, S., Marsch, L. A., Hancock, J. T., Das, A. K. 2017; 19 (10): e353

    Abstract

    Substance use-related communication for drug use promotion and its prevention is widely prevalent on social media. Social media big data involve naturally occurring communication phenomena that are observable through social media platforms, which can be used in computational or scalable solutions to generate data-driven inferences. Despite the promising potential to utilize social media big data to monitor and treat substance use problems, the characteristics, mechanisms, and outcomes of substance use-related communications on social media are largely unknown. Understanding these aspects can help researchers effectively leverage social media big data and platforms for observation and health communication outreach for people with substance use problems.The objective of this critical review was to determine how social media big data can be used to understand communication and behavioral patterns of problematic use of prescription drugs. We elaborate on theoretical applications, ethical challenges and methodological considerations when using social media big data for research on drug abuse and addiction. Based on a critical review process, we propose a typology with key initiatives to address the knowledge gap in the use of social media for research on prescription drug abuse and addiction.First, we provided a narrative summary of the literature on drug use-related communication on social media. We also examined ethical considerations in the research processes of (1) social media big data mining, (2) subgroup or follow-up investigation, and (3) dissemination of social media data-driven findings. To develop a critical review-based typology, we searched the PubMed database and the entire e-collection theme of "infodemiology and infoveillance" in the Journal of Medical Internet Research / JMIR Publications. Studies that met our inclusion criteria (eg, use of social media data concerning non-medical use of prescription drugs, data informatics-driven findings) were reviewed for knowledge synthesis. User characteristics, communication characteristics, mechanisms and predictors of such communications, and the psychological and behavioral outcomes of social media use for problematic drug use-related communications are the dimensions of our typology. In addition to ethical practices and considerations, we also reviewed the methodological and computational approaches used in each study to develop our typology.We developed a typology to better understand non-medical, problematic use of prescription drugs through the lens of social media big data. Highly relevant studies that met our inclusion criteria were reviewed for knowledge synthesis. The characteristics of users who shared problematic substance use-related communications on social media were reported by general group terms, such as adolescents, Twitter users, and Instagram users. All reviewed studies examined the communication characteristics, such as linguistic properties, and social networks of problematic drug use-related communications on social media. The mechanisms and predictors of such social media communications were not directly examined or empirically identified in the reviewed studies. The psychological or behavioral consequence (eg, increased behavioral intention for mimicking risky health behaviors) of engaging with and being exposed to social media communications regarding problematic drug use was another area of research that has been understudied.We offer theoretical applications, ethical considerations, and empirical evidence within the scope of social media communication and prescription drug abuse and addiction. Our critical review suggests that social media big data can be a tremendous resource to understand, monitor and intervene on drug abuse and addiction problems.

    View details for PubMedID 29089287

  • How Advertorials Deactivate Advertising Schema: MTurk-Based Experiments to Examine Persuasion Tactics and Outcomes in Health Advertisements COMMUNICATION RESEARCH Kim, S., Hancock, J. T. 2017; 44 (7): 1019–45
  • Your post is embarrassing me: Face threats, identity, and the audience on Facebook COMPUTERS IN HUMAN BEHAVIOR Oeldorf-Hirsch, A., Birnholtz, J., Hancock, J. T. 2017; 73: 92-99
  • Should I Share That? Prompting Social Norms That Influence Privacy Behaviors on a Social Networking Site JOURNAL OF COMPUTER-MEDIATED COMMUNICATION Spottswood, E. L., Hancock, J. T. 2017; 22 (2): 55-70

    View details for DOI 10.1111/jcc4.12182

    View details for Web of Science ID 000397094200001

  • Self-Disclosure and Perceived Trustworthiness of Airbnb Host Profiles Ma, X., Hancock, J. T., Mingjie, K., Naaman, M., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2017: 2397-2409
  • Linguistic analysis of chat transcripts from child predator undercover sex stings JOURNAL OF FORENSIC PSYCHIATRY & PSYCHOLOGY Drouin, M., Boyd, R. L., Hancock, J. T., James, A. 2017; 28 (4): 437-457
  • The 27 Club: Music Lyrics Reflect Psychological Distress COMMUNICATION REPORTS Markowitz, D. M., Hancock, J. T. 2017; 30 (1): 1-13
  • The positivity bias and prosocial deception on facebook COMPUTERS IN HUMAN BEHAVIOR Spottswood, E. L., Hancock, J. T. 2016; 65: 252-259
  • Online dating system design and relational decision making: Choice, algorithms, and control PERSONAL RELATIONSHIPS Tong, S. T., Hancock, J. T., Slatcher, R. B. 2016; 23 (4): 645-662

    View details for DOI 10.1111/pere.12158

    View details for Web of Science ID 000390564800002

  • Linguistic Obfuscation in Fraudulent Science JOURNAL OF LANGUAGE AND SOCIAL PSYCHOLOGY Markowitz, D. M., Hancock, J. T. 2016; 35 (4): 435-445
  • Computer-Mediated Deception: Strategies Revealed by Language-Action Cues in Spontaneous Communication JOURNAL OF MANAGEMENT INFORMATION SYSTEMS Ho, S. M., Hancock, J. T., Booth, C., Liu, X. 2016; 33 (2): 393-420
  • Effects of Network Connections on Deception and Halo Effects in Linkedin PSYCHOLOGY OF SOCIAL NETWORKING: PERSONAL EXPERIENCE IN ONLINE COMMUNITIES Guillory, J. E., Hancock, J. T., Riva, G., Wiederhold, B. K., Cipresso, P. 2016: 66-77
  • The Influence of Technology on Romantic Relationships: Understanding Online Dating Tong, S., Hancock, J. T., Slatcher, R. B., Meiselwitz, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2016: 162-173
  • Demystifying Insider Threat: Language-Action Cues in Group Dynamics Ho, S., Hancock, J. T., Booth, C., Burmester, M., Liu, X., Timmarajus, S. S., Bui, T. X., Sprague, R. H. IEEE COMPUTER SOC. 2016: 2729-2738
  • Real or Spiel? A Decision Tree Approach for Automated Detection of Deceptive Language-Action Cues Ho, S., Hancock, J. T., Booth, C., Liu, X., Liu, M., Timmarajus, S. S., Burmester, M., Bui, T. X., Sprague, R. H. IEEE COMPUTER SOC. 2016: 3706-3715