About

Cristian Danescu-Niculescu-Mizil is an associate professor of information science at Cornell University. His research focuses on developing computational frameworks to better understand human social behavior by analyzing large amounts of natural language data generated online. His work involves areas such as data science, human-centered natural language processing, artificial intelligence, computational approaches to studying people and society, computational social sciences, machine learning, and natural language processing. He has received several awards and honors, including a National Science Foundation CAREER Award and a Google Research Faculty Award. His research has been featured in various popular media outlets such as the New Scientist, NBC's The Today Show, NPR, BBC, WIRED, the New York Times, Wall Street Journal, and Washington Post. For more information about his publications, recent updates, and additional activities, his webpage can be visited.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Engineering
• Communication
• World Wide Web
• Internet privacy
• Cognitive science

Selected publications

• Conversations Gone Awry, But Then? Evaluating Conversational Forecasting Models

  ArXiv.org · 2025-07-25

  preprintOpen accessSenior author

  We often rely on our intuition to anticipate the direction of a conversation. Endowing automated systems with similar foresight can enable them to assist human-human interactions. Recent work on developing models with this predictive capacity has focused on the Conversations Gone Awry (CGA) task: forecasting whether an ongoing conversation will derail. In this work, we revisit this task and introduce the first uniform evaluation framework, creating a benchmark that enables direct and reliable comparisons between different architectures. This allows us to present an up-to-date overview of the current progress in CGA models, in light of recent advancements in language modeling. Our framework also introduces a novel metric that captures a model's ability to revise its forecast as the conversation progresses.

  PublisherOA PDFDOI

• Hanging in the Balance: Pivotal Moments in Crisis Counseling Conversations

  2025-01-01

  articleOpen accessSenior author

  During a conversation, there can come certain moments where its outcome hangs in the balance.In these pivotal moments, how one responds can put the conversation on substantially different trajectories leading to significantly different outcomes.Systems that can detect when such moments arise could assist conversationalists in domains with highly consequential outcomes, such as mental health crisis counseling.In this work, we introduce an unsupervised computational method for detecting such pivotal moments as they happen.The intuition is that a moment is pivotal if our expectation of the conversation's outcome varies widely depending on what might be said next.By applying our method to crisis counseling conversations, we first validate it by showing that it aligns with human perception-counselors take significantly longer to respond during moments detected by our method-and with the eventual conversational trajectory-which is more likely to change course at these times.We then use our framework to explore the relation between the counselor's response during pivotal moments and the eventual outcome of the session.

  PublisherOA PDFDOI

• Time is On My Side: Dynamics of Talk-Time Sharing in Video-chat Conversations

  ArXiv.org · 2025-06-25

  preprintOpen accessSenior author

  An intrinsic aspect of every conversation is the way talk-time is shared between multiple speakers. Conversations can be balanced, with each speaker claiming a similar amount of talk-time, or imbalanced when one talks disproportionately. Such overall distributions are the consequence of continuous negotiations between the speakers throughout the conversation: who should be talking at every point in time, and for how long? In this work we introduce a computational framework for quantifying both the conversation-level distribution of talk-time between speakers, as well as the lower-level dynamics that lead to it. We derive a typology of talk-time sharing dynamics structured by several intuitive axes of variation. By applying this framework to a large dataset of video-chats between strangers, we confirm that, perhaps unsurprisingly, different conversation-level distributions of talk-time are perceived differently by speakers, with balanced conversations being preferred over imbalanced ones, especially by those who end up talking less. Then we reveal that -- even when they lead to the same level of overall balance -- different types of talk-time sharing dynamics are perceived differently by the participants, highlighting the relevance of our newly introduced typology. Finally, we discuss how our framework offers new tools to designers of computer-mediated communication platforms, for both human-human and human-AI communication.

  PublisherOA PDFDOI

• Hanging in the Balance: Pivotal Moments in Crisis Counseling Conversations

  ArXiv.org · 2025-06-04

  preprintOpen accessSenior author

  During a conversation, there can come certain moments where its outcome hangs in the balance. In these pivotal moments, how one responds can put the conversation on substantially different trajectories leading to significantly different outcomes. Systems that can detect when such moments arise could assist conversationalists in domains with highly consequential outcomes, such as mental health crisis counseling. In this work, we introduce an unsupervised computational method for detecting such pivotal moments as they happen, in an online fashion. Our approach relies on the intuition that a moment is pivotal if our expectation of the outcome varies widely depending on what might be said next. By applying our method to crisis counseling conversations, we first validate it by showing that it aligns with human perception -- counselors take significantly longer to respond during moments detected by our method -- and with the eventual conversational trajectory -- which is more likely to change course at these times. We then use our framework to explore the relation of the counselor's response during pivotal moments with the eventual outcome of the session.

  PublisherOA PDFDOI

• A Similarity Measure for Comparing Conversational Dynamics

  2025-01-01

  articleOpen accessSenior author

  The quality of a conversation goes beyond the individual quality of each reply, and instead emerges from how these combine into interactional dynamics that give the conversation its distinctive overall "shape".However, there is no robust automated method for comparing conversations in terms of their overall dynamics.Such methods could enhance the analysis of conversational data and help evaluate conversational agents more holistically.In this work, we introduce a similarity measure for comparing conversations with respect to their dynamics.We design a validation procedure for testing the robustness of the metric in capturing differences in conversation dynamics and for assessing its sensitivity to the topic of the conversations.To illustrate the measure's utility, we use it to analyze conversational dynamics in a large online community, bringing new insights into the role of situational power in conversations.

  PublisherOA PDFDOI

• Time is On My Side: Dynamics of Talk-Time Sharing in Video-chat Conversations

  Proceedings of the ACM on Human-Computer Interaction · 2025-10-16

  articleOpen accessSenior author

  An intrinsic aspect of every conversation is the way talk-time is shared between multiple speakers. Conversations can be balanced, with each speaker claiming a similar amount of talk-time, or imbalanced when one talks disproportionately. Such overall distributions are the consequence of continuous negotiations between the speakers throughout the conversation: who should be talking at every point in time, and for how long? In this work we introduce a computational framework for quantifying both the conversation-level distribution of talk-time between speakers, as well as the lower-level dynamics that lead to it. We derive a typology of talk-time sharing dynamics structured by several intuitive axes of variation. By applying this framework to a large dataset of video-chats between strangers, we confirm that, perhaps unsurprisingly, different conversation-level distributions of talk-time are perceived differently by speakers, with balanced conversations being preferred over imbalanced ones, especially by those who end up talking less. Then we reveal that--even when they lead to the same level of overall balance--different types of talk-time sharing dynamics are perceived differently by the participants, highlighting the relevance of our newly introduced typology. Finally, we discuss how our framework offers new tools to designers of computer-mediated communication platforms, for both human-human and human-AI communication.

  PublisherDOI

• A Similarity Measure for Comparing Conversational Dynamics

  ArXiv.org · 2025-07-25

  preprintOpen accessSenior author

  The quality of a conversation goes beyond the individual quality of each reply, and instead emerges from how these combine into interactional dynamics that give the conversation its distinctive overall "shape". However, there is no robust automated method for comparing conversations in terms of their overall dynamics. Such methods could enhance the analysis of conversational data and help evaluate conversational agents more holistically. In this work, we introduce a similarity measure for comparing conversations with respect to their dynamics. We design a validation procedure for testing the robustness of the metric in capturing differences in conversation dynamics and for assessing its sensitivity to the topic of the conversations. To illustrate the measure's utility, we use it to analyze conversational dynamics in a large online community, bringing new insights into the role of situational power in conversations.

  PublisherOA PDFDOI

• How Did We Get Here? Summarizing Conversation Dynamics

  arXiv (Cornell University) · 2024-04-29

  preprintOpen accessSenior author

  Throughout a conversation, the way participants interact with each other is in constant flux: their tones may change, they may resort to different strategies to convey their points, or they might alter their interaction patterns. An understanding of these dynamics can complement that of the actual facts and opinions discussed, offering a more holistic view of the trajectory of the conversation: how it arrived at its current state and where it is likely heading. In this work, we introduce the task of summarizing the dynamics of conversations, by constructing a dataset of human-written summaries, and exploring several automated baselines. We evaluate whether such summaries can capture the trajectory of conversations via an established downstream task: forecasting whether an ongoing conversation will eventually derail into toxic behavior. We show that they help both humans and automated systems with this forecasting task. Humans make predictions three times faster, and with greater confidence, when reading the summaries than when reading the transcripts. Furthermore, automated forecasting systems are more accurate when constructing, and then predicting based on, summaries of conversation dynamics, compared to directly predicting on the transcripts.

  PublisherOA PDFDOI

• How did we get here? Summarizing conversation dynamics

  2024-01-01 · 2 citations

  articleOpen accessSenior author

  Yilun Hua, Nicholas Chernogor, Yuzhe Gu, Seoyeon Jeong, Miranda Luo, Cristian Danescu-Niculescu-Mizil. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

  PublisherOA PDFDOI

• Taking a turn for the better: Conversation redirection throughout the course of mental-health therapy

  2024-01-01

  articleOpen accessSenior author

  Mental-health therapy involves a complex conversation flow in which patients and therapists continuously negotiate what should be talked about next.For example, therapists might try to shift the conversation's direction to keep the therapeutic process on track and avoid stagnation, or patients might push the discussion towards issues they want to focus on.How do such patient and therapist redirections relate to the development and quality of their relationship?To answer this question, we introduce a probabilistic measure of the extent to which a certain utterance immediately redirects the flow of the conversation, accounting for both the intention and the actual realization of such a change.We apply this new measure to characterize the development of patienttherapist relationships over multiple sessions in a very large, widely-used online therapy platform.Our analysis reveals that (1) patient control of the conversation's direction generally increases relative to that of the therapist as their relationship progresses; and (2) patients who have less control in the first few sessions are significantly more likely to eventually express dissatisfaction with their therapist and terminate the relationship.

  PublisherOA PDFDOI

Frequent coauthors

• Lillian Lee

  36 shared

• Justine Zhang

  University College London

  25 shared

• Jure Leskovec

  Stanford University

  21 shared

• Jon Kleinberg

  Cornell University

  17 shared

• Jonathan P. Chang

  Cornell University

  17 shared

• Lucas Dixon

  15 shared

• Dario Taraborelli

  Chan Zuckerberg Initiative (United States)

  15 shared

• Yiqing Hua

  Google (United States)

  15 shared

Awards & honors

• NSF Faculty Early Career Development Award (CAREER)
• Google Research Faculty Award
• ICWSM Test of Time Award