Abstract TED talks are a popular internet forum where new ideas and research are presented by a wide variety of speakers. In this study, we investigated how the language used in TED talks influenced popularity and viewer ratings. We also investigated the differences in linguistic style and ratings of talks given by academics and non‐academics. The transcripts of 1866 talks were analyzed using the Linguistic Inquiry and Word Count program and eight language variables were correlated with number of views and viewer ratings. We found that talks with more analytic language received fewer views, while a greater use of the pronoun “I,” positive emotion and social words was associated with more views. Talks with these linguistic characteristics received more emotional viewer ratings such as inspiring or courageous. When comparing talks by academics and non‐academics, there was no difference in the overall popularity but viewers rated talks by academics as more fascinating, informative, and persuasive while non‐academics received higher emotional ratings. The implications for understanding social influence processes are discussed.

We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release Hippocorpus, a dataset of 7,000 stories about imagined and recalled events. We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory (Tulving, 1972) for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events. Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly; Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language.

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.

Personal writings have inspired researchers in the fields of linguistics and psychology to study the relationship between language and culture to better understand the psychology of people across different cultures. In this paper, we explore this relation by developing cross-cultural word models to identify words with cultural bias – i.e., words that are used in significantly different ways by speakers from different cultures. Focusing specifically on two cultures: United States and Australia, we identify a set of words with significant usage differences, and further investigate these words through feature analysis and topic modeling, shedding light on the attributes of language that contribute to these differences.

The words people use in their conversations, emails, and diaries can tell us how they think, approach problems, connect with others, and their behaviors. Of particular interest are people's use of function words -- pronouns, articles, and other small and forgettable words. Processed in the brain differently from content words, function words reveal where people are paying attention and how they think about themselves and others. After summarizing dozens of studies on language and psychological state, the talk will explore how text analysis can help us get inside the heads of the people we study.

We develop and evaluate a real-time language feedback system that monitors the communication patterns among students in a discussion group and provides real-time instructions to shape the way the group works together. As an initial step, we determine which group processes are related to better outcomes. We then experimentally test the efficacy of providing real-time instructions which target two of these group processes. The feedback system was successfully able to shape the way groups worked together. However, only appropriate feedback given to groups that were not working well together from the start was able to improve group performance.

Why do people contribute content to communities of question-answering, such as Yahoo! Answers? We investigated this issue on MathOverflow, a site dedicated to research-level mathematics, in which users ask and answer questions. MathOverflow is the first in a growing number of specialized Q&A sites using the Stack Exchange platform for scientific collaboration. In this study we combine responses to a survey with collected data on posting behavior on the site. User behavior suggests that building reputation is an important incentive, even though users do not report this in the survey. Level of expertise affects users' reported motivation to help others, but does not affect the importance of reputation building. We discuss the implications for the design of communities to target and encourage more contributions.

There are two perspectives on the role of reputation in collaborative online projects such as Wikipedia or Yahoo! Answers. One, user reputation should be minimized in order to increase the number of contributions from a wide user base. Two, user reputation should be used as a heuristic to identify and promote high quality contributions. The current study examined how offline and online reputations of contributors affect perceived quality in MathOverflow, an online community with 3470 active users. On MathOverflow, users post high-level mathematics questions and answers. Community members also rate the quality of the questions and answers. This study is unique in being able to measure offline reputation of users. Both offline and online reputations were consistently and independently related to the perceived quality of authors' submissions, and there was only a moderate correlation between established offline and newly developed online reputation.

In this note we introduce a new methodology that combines tools from social language processing and network analysis to identify socially situated relationships between individuals, even when these relationships are latent or unrecognized. We call this approach social language network analysis (SLNA). We describe the philosophical antecedents of SLNA, the mechanics of preprocessing, processing, and post-processing stages, and the results of applying this approach to a 15-month corporate discussion archive. These example results include an explicit mapping of both the perceived expertise hierarchy and the social support / friendship network within this group. Author Keywords social language processing, social network analysis, network