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.

Throughout a conversation, participants make choices that can orient the flow of the interaction.Such choices are particularly salient in the consequential domain of crisis counseling, where a difficulty for counselors is balancing between two key objectives: advancing the conversation towards a resolution, and empathetically addressing the crisis situation.In this work, we develop an unsupervised methodology to quantify how counselors manage this balance.Our main intuition is that if an utterance can only receive a narrow range of appropriate replies, then its likely aim is to advance the conversation forwards, towards a target within that range.Likewise, an utterance that can only appropriately follow a narrow range of possible utterances is likely aimed backwards at addressing a specific situation within that range.By applying this intuition, we can map each utterance to a continuous orientation axis that captures the degree to which it is intended to direct the flow of the conversation forwards or backwards.This unsupervised method allows us to characterize counselor behaviors in a large dataset of crisis counseling conversations, where we show that known counseling strategies intuitively align with this axis.We also illustrate how our measure can be indicative of a conversation's progress, as well as its effectiveness.

Jonathan P. Chang, Caleb Chiam, Liye Fu, Andrew Wang, Justine Zhang, Cristian Danescu-Niculescu-Mizil. Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2020.

Understanding what leads to effective conversations can aid the design of better computer-mediated communication platforms. In particular, prior observational work has sought to identify behaviors of individuals that correlate to their conversational efficiency. However, translating such correlations to causal interpretations is a necessary step in using them in a prescriptive fashion to guide better designs and policies. In this work, we formally describe the problem of drawing causal links between conversational behaviors and outcomes. We focus on the task of determining a particular type of policy for a text-based crisis counseling platform: how best to allocate counselors based on their behavioral tendencies exhibited in their past conversations. We apply arguments derived from causal inference to underline key challenges that arise in conversational settings where randomized trials are hard to implement. Finally, we show how to circumvent these inference challenges in our particular domain, and illustrate the potential benefits of an allocation policy informed by the resulting prescriptive information.

When people communicate with each other, their choice of what to say is tied to their perceptions of the audience. For many communication channels, people have some ability to explicitly specify their audience members and the different roles they can play. While existing accounts of communication behavior have largely focused on how people tailor the content of their messages, we focus on the configuring of the audience as a complementary family of decisions in communication. We formulate a general description of audience configuration choices, highlighting key aspects of the audience that people could configure to reflect a range of communicative goals. We then illustrate these ideas via a case study of email usage-a realistic domain where audience configuration choices are particularly fine-grained and explicit in how email senders fill the To and Cc address fields. In a large collection of enterprise emails, we explore how people configure their audiences, finding salient patterns relating a sender's choice of configuration to the types of participants in the email exchange, the content of the message, and the nature of the subsequent interactions. Our formulation and findings show how analyzing audience configurations can enrich and extend existing accounts of communication behavior, and frame research directions on audience configuration decisions in communication and collaboration.

Mental health counseling is an enterprise with profound societal importance where conversations play a primary role.In order to acquire the conversational skills needed to face a challenging range of situations, mental health counselors must rely on training and on continued experience with actual clients.However, in the absence of large scale longitudinal studies, the nature and significance of this developmental process remain unclear.For example, prior literature suggests that experience might not translate into consequential changes in counselor behavior.This has led some to even argue that counseling is a profession without expertise.In this work, we develop a computational framework to quantify the extent to which individuals change their linguistic behavior with experience and to study the nature of this evolution.We use our framework to conduct a large longitudinal study of mental health counseling conversations, tracking over 3,400 counselors across their tenure.We reveal that overall, counselors do indeed change their conversational behavior to become more diverse across interactions, developing an individual voice that distinguishes them from other counselors.Furthermore, a finer-grained investigation shows that the rate and nature of this diversification vary across functionally different conversational components.

Justine Zhang, Jonathan Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Dario Taraborelli, Nithum Thain. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.

Public discussions on social media platforms are an intrinsic part of online information consumption. Characterizing the diverse range of discussions which can arise is crucial for these platforms, as they may seek to organize and curate them. This paper introduces a framework to characterize public discussions, relying on a representation that captures a broad set of social patterns which emerge from the interactions between interlocutors, comments, and audience reactions. We apply our framework to study public discussions on Facebook at two complementary scales. First, at the level of individual discussions, we use it to predict a discussion's future trajectory, anticipating future antisocial actions (such as participants blocking each other) and forecasting the discussion's growth. Second, we systematically analyze the variation of discussions across thousands of Facebook sub-communities, revealing subtle differences (and unexpected similarities) in how people interact when discussing online content. We further show that this variation is driven more by participant tendencies than by the content triggering these discussions.

Questions play a prominent role in social interactions, performing rhetorical functions that go beyond that of simple informational exchange.The surface form of a question can signal the intention and background of the person asking it, as well as the nature of their relation with the interlocutor.While the informational nature of questions has been extensively examined in the context of question-answering applications, their rhetorical aspects have been largely understudied.In this work we introduce an unsupervised methodology for extracting surface motifs that recur in questions, and for grouping them according to their latent rhetorical role.By applying this framework to the setting of question sessions in the UK parliament, we show that the resulting typology encodes key aspects of the political discourse-such as the bifurcation in questioning behavior between government and opposition parties-and reveals new insights into the effects of a legislator's tenure and political career ambitions.

Justine Zhang, Ravi Kumar, Sujith Ravi, Cristian Danescu-Niculescu-Mizil. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.

Given the extremely large pool of events and stories available, media outlets need to focus on a subset of issues and aspects to convey to their audience. Outlets are often accused of exhibiting a systematic bias in this selection process, with different outlets portraying different versions of reality. However, in the absence of objective measures and empirical evidence, the direction and extent of systematicity remains widely disputed. In this paper we propose a framework based on quoting patterns for quantifying and characterizing the degree to which media outlets exhibit systematic bias. We apply this framework to a massive dataset of news articles spanning the six years of Obama's presidency and all of his speeches, and reveal that a systematic pattern does indeed emerge from the outlet's quoting behavior. Moreover, we show that this pattern can be successfully exploited in an unsupervised prediction setting, to determine which new quotes an outlet will select to broadcast. By encoding bias patterns in a low-rank space we provide an analysis of the structure of political media coverage. This reveals a latent media bias space that aligns surprisingly well with political ideology and outlet type. A linguistic analysis exposes striking differences across these latent dimensions, showing how the different types of media outlets portray different realities even when reporting on the same events. For example, outlets mapped to the mainstream conservative side of the latent space focus on quotes that portray a presidential persona disproportionately characterized by negativity.