Linguistic style accommodation between conversationalists is associated with positive social outcomes. We examine social power and personality as factors driving the occurrence of linguistic style accommodation, and the social outcomes of accommodation. Social power was manipulated to create 144 face‐to‐face dyadic interactions between individuals of high versus low power and 64 neutral power interactions. Particular configurations of personality traits (high self‐monitoring, Machiavellianism and leadership, and low self‐consciousness, impression management and agreeableness), combined with a low‐power role, led to an increased likelihood of linguistic style accommodation. Further, greater accommodation by low‐power individuals positively influenced perceptions of subjective rapport and attractiveness. We propose individual differences interact with social context to influence the conditions under which nonconscious communication accommodation occurs.

Communication accommodation theory predicts that social power plays an important role in influencing communicative behaviors. Previous research suggests these effects extend to linguistic style, thought to be a non-conscious aspect of communication. Here, we explore if these effects hold when individuals converse using a medium limited in personal cues, computer-mediated-communication (CMC). We manipulated social power in instant messaging conversations and measured subsequent interpersonal impressions. Low power induced greater likelihood of linguistic style accommodation, across between-(Study 1) and within-subjects (Study 2) experiments. Accommodation by those in a low power role had no impact on impressions formed by their partner. In contrast, linguistic style accommodation by individuals in a high-power role was associated with negative interpersonal impressions formed by their lower power partner. The results show robust effects of power in shaping language use across CMC. Further, the interpersonal effects of linguistic accommodation depend upon the conversational norms of the social context.

Categorization of text in IR has traditionally focused on topic. As use of the Internet and e−mail increases, categorization has become a key area of research as users demand methods of prioritizing documents. This work investigates text classification by format style, i.e. "genre", and demonstrates, by complementing topic classification, that it can significantly improve retrieval of information. The paper compares use of presentation features to word features, and the combination thereof, using Naïve Bayes, C4.5 and SVM classifiers. Results show use of combined feature sets with SVM yields 92% classification accuracy in sorting seven genres. IntroductionThis paper firstly defines genre, explains the rationale for automatic genre classification, and reviews some previously published work relevant to this problem. It describes the features chosen to be extracted from documents for input to a classification system. The paper next describes data used, experiments carried out, and the results obtained. Finally the paper discusses the results and suggests ways for the research to progress. Defining GenreThe genre of a document is defined here as a label which denotes a set of conventions in the way in which information is presented. These conventions cover both formatting and style of language used. Examples of genres include "Newswire", "Classified Advertisements", and "Radio Broadcast News Transcript". The format of the text and the style of language used within a genre is usually consistent even though the topics of different documents may vary greatly. Note that text classifications such as "Sport" or "Politics" are not considered as genres here since these are broad topic areas. Why Genre?Many people are experiencing the growth in the volume of electronic text: Sources include news services, online journals, and e−mail. Few people have time to scan every text source of potential interest to them and not all sources are of equal interest to everyone. The continuing expansion of the Internet makes it increasingly hard to find information relevant to the user's needs. Search engines go some way to solving this problem, but often the results are dominated by hits that do not match the user's requirements. Many search engines, such as Yahoo, provide a hierarchical classification of sites which organize web sites by the type of information and/or services they provide. However, the hierarchies only cover a fraction of the Web and are largely hand built. An automatic method of building site categories, in conjunction with topic identification, would speed the hierarchy construction and allow more frequent updates.The authors believe that a classifier can be trained to distinguish different document classes, or genres, such as advertisements or jokes from news stories, for example. It can be trained to help identify the proportion of user− relevant texts, which can often be very small. If a user is searching for "stories of the god Jupiter" then news articles and scientific papers would less likely be of intere...

Strategic word mimicry during negotiations facilitates better outcomes. We explore mimicry of specific word categories and perceptions of rapport, trust, and liking as underlying mechanisms. Dyads took part in an online negotiation exercise in which word mimicry was manipulated: Participants were instructed to mimic each other’s words (both‐mimic), one participant mimicked the other (half‐mimic), or neither participant mimicked (neither‐mimic). When given a simple instruction to mimic their partner, participants mimicked both the style (personal pronouns, adverbs, linguistic style, interrogative terms) and the content (affiliation terms, power terms, and assents) of their partner’s messages. Mimicry was associated with greater joint and individual points gain and perceptions of rapport from the mimicked partner. Further, mimicry of interrogative terms (e.g., how, why) mediated positive effects of mimicry upon negotiation outcomes, suggesting the coordination of question asking between negotiators is an important strategy to create beneficial interactions and add value in negotiations.

Categorization of text in IR has traditionally focused on topic. As use of the Internet and e−mail increases, categorization has become a key area of research as users demand methods of prioritizing documents. This work investigates text classification by format style, i.e. "genre", and demonstrates, by complementing topic classification, that it can significantly improve retrieval of information. The paper compares use of presentation features to word features, and the combination thereof, using Naïve Bayes, C4.5 and SVM classifiers. Results show use of combined feature sets with SVM yields 92% classification accuracy in sorting seven genres.