Academic Appointments


  • Professor, Communication

Program Affiliations


  • Symbolic Systems Program

2018-19 Courses


Stanford Advisees


All Publications


  • 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
  • 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
  • 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