About

Ryan L. Boyd is an Associate Research Professor of Computer Science at Stony Brook University. He earned his Ph.D. from The University of Texas at Austin in 2017. His research employs computational social science methods to understand how verbal behavior provides clues to how individuals think, feel, and behave. Dr. Boyd's work spans a broad range of topics including personality, society, mental health, human sexuality, and storytelling. He has authored numerous free, open-source text analysis applications for social scientists and is a leading scholar behind the Linguistic Inquiry and Word Count software. Dr. Boyd serves on the editorial boards of several cross-disciplinary journals, including Frontiers in Artificial Intelligence: Language and Computation and Frontiers in Social Psychology: Computational Social Science. He is also a member of the advisory board for the journal Psychology of Language and Communication. His current research focuses on foundational psychological processes such as motives, emotion, and cognition; the relationship between personality and verbal behavior; health and well-being issues including psychopathology, suicidality, and substance use; and social dynamics at various scales, from individual interactions to societal phenomena.

Research topics

• Computer Science
• Sociology
• Psychology
• Social psychology
• Cognitive psychology
• Epistemology
• Social Science
• Cognitive science
• Natural Language Processing
• Linguistics
• Data science
• Philosophy
• World Wide Web
• Psychotherapist
• Internal medicine
• Telecommunications
• Medicine
• Clinical psychology
• Mathematics
• Geometry

Selected publications

• Values in Words: Using Language to Evaluate and Understand Personal Values

  Proceedings of the International AAAI Conference on Web and Social Media · 2021 · 126 citations

  1st authorCorresponding
  • Computer Science
  • Psychology
  • Computer Science

  People's values provide a decision-making framework that helps guide their everyday actions. Most popular methods of assessing values show tenuous relationships with everyday behaviors. Using a new Amazon Mechanical Turk dataset (N = 767) consisting of people's language, values, and behaviors, we explore the degree to which attaining "ground truth" is possible with regards to such complicated mental phenomena. We then apply our findings to a corpus of Facebook user (N=130,828) status updates in order to understand how core values influence the personal thoughts and behaviors that users share through social media. Our findings suggest that self-report questionnaires for abstract and complex phenomena, such as values, are inadequate for painting an accurate picture of individual mental life. Free response language data and language modeling show greater promise for understanding both the structure and content of concepts such as values and, additionally, exhibit a predictive edge over self-report questionnaires.

  DOI

• The narrative arc: Revealing core narrative structures through text analysis

  Science Advances · 2020 · 172 citations

  1st authorCorresponding
  • Computer Science
  • Natural Language Processing
  • Computer Science

  Scholars across disciplines have long debated the existence of a common structure that underlies narratives. Using computer-based language analysis methods, several structural and psychological categories of language were measured across ~40,000 traditional narratives (e.g., novels and movie scripts) and ~20,000 nontraditional narratives (science reporting in newspaper articles, TED talks, and Supreme Court opinions). Across traditional narratives, a consistent underlying story structure emerged that revealed three primary processes: staging, plot progression, and cognitive tension. No evidence emerged to indicate that adherence to normative story structures was related to the popularity of the story. Last, analysis of fact-driven texts revealed structures that differed from story-based narratives.

  DOI

• Understanding breast cancer survivors’ information-seeking behaviours and overall experiences: a comparison of themes derived from social media posts and focus groups

  Psychology and Health · 2020 · 21 citations

  • Sociology
  • Psychology
  • Clinical psychology

  OBJECTIVE: Using two different analysis techniques, this study explored differences and similarities in information-seeking discourse and overall breast cancer experiences between posters to a Reddit board and breast cancer survivor focus groups. DESIGN: = 18). Finally, themes derived from each analysis method were compared. MAIN OUTCOME MEASURES: Outcome measures include themes extracted from Reddit posts and themes generated from breast cancer survivor focus groups. RESULTS: Findings between qualitative methodologies represent similar yet nuanced themes in survivors' discourse. The MEM resulted in seven themes: diagnosis, treatment process, social support, existentialism, risk, information-seeking and surgery. Focus groups revealed the same initial four MEM themes plus the following: disclosure, coping and fears. CONCLUSIONS: The MEM is a cost-effective research mechanism for informing common themes of experiences of cancer patients and survivors and may offer initial data to guide psychosocial oncology research design and recruitment.

  DOI

• Natural Language Analysis and the Psychology of Verbal Behavior: The Past, Present, and Future States of the Field

  Journal of Language and Social Psychology · 2020 · 299 citations

  1st authorCorresponding
  • Social Science
  • Computer Science
  • Sociology

  Throughout history, scholars and laypeople alike have believed that our words contain subtle clues about what we are like as people, psychologically speaking. However, the ways in which language has been used to infer psychological processes has seen dramatic shifts over time and, with modern computational technologies and digital data sources, we are on the verge of a massive revolution in language analysis research. In this article, we discuss the past and current states of research at the intersection of language analysis and psychology, summarizing the central successes and shortcomings of psychological text analysis to date. We additionally outline and discuss a critical need for language analysis practitioners in the social sciences to expand their view of verbal behavior. Lastly, we discuss the trajectory of interdisciplinary research on language and the challenges of integrating analysis methods across paradigms, recommending promising future directions for the field along the way.

  DOI

Frequent coauthors

• James W. Pennebaker

  The University of Texas at Austin

  36 shared

• Vinaya Manchaiah

  University of Colorado Denver

  31 shared

• Michael D. Robinson

  North Dakota State University

  19 shared

• Amelia M. Stanton

  Boston University

  16 shared

• Rada Mihalcea

  15 shared

• Steven R. Wilson

  Oakland University

  12 shared

• Cindy M. Meston

  The University of Texas at Austin

  10 shared

• Aniruddha K. Deshpande

  Hofstra University

  9 shared

Education

• Ph.D., Psychology

  University of Texas at Austin

  2017

• M.S., Psychology

  North Dakota State University

  2013

• B.A., Psychology

  Purdue University, Fort Wayne

  2010

Similar researchers at Stony Brook University

• Scott McLennanh-145
• Anatoly I. Frenkelh-101
• Vitaly Citovskyh-98
• Michael R. Douglash-91
• Minghua Zhangh-89