Mental health counseling is an enterprise with profound societ al 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.
   Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity – especially with regard to race – plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified “netspeak” dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English. In this paper we investigate the connection between language and community membership of long time community participants through computational modeling techniques. We report on findings from an analysis of language usage within a popular online discussion forum with participation of thousands of users spanning multiple years. We find community norms of long time participants that are characterized by forum specific jargon and a style that is highly informal and shows familiarity with specific other participants and high emotional involvement in the discussion. We also find quantitative evidence of persistent shifts in language usage towards these norms across users over the course of the first year of community participation. Our observed patterns suggests language stabilization after 8 or 9 months of participation. We present a study of the relationship between gender, linguistic style, and social networks, using a novel corpus of 14,000 Twitter users. Prior quantitative work on gender often treats this social variable as a female male binary; we argue for a more nuanced approach. By clustering Twitter users, we find a natural decomposition of the dataset into various styles and topical interests. Many clusters have strong gender orientations, but their use of linguistic resources sometimes directly conflicts with the population-level language statistics. We view these clusters as a more accurate reflection of the multifaceted nature of gendered language styles. Previous corpus-based work has also had little to say about individuals whose linguistic styles defy population-level gender patterns. To identify such individuals, we train a statistical classifier, and measure the classifier confidence for each individual in the dataset. Examining individuals whose language does not match the classifier's model for their gender, we find that they have social networks that include significantly fewer same-gender social connections and that, in general, social network homophily is correlated with the use of same-gender language markers. Pairing computational methods and social theory thus offers a new perspective on how gender emerges as individuals position themselves relative to audiences, topics, and mainstream gender norms. We investigate how novel English-derived words (anglicisms) are used in a German-language Internet hip hop forum, and what factors contribute to their uptake. Vibrant online communities are in constant flux. As members join and depart, the interactional norms evolve, stimulating further changes to the membership and its social dynamics. Linguistic change --- in the sense of innovation that becomes accepted as the norm --- is essential to this dynamic process: it both facilitates individual expression and fosters the emergence of a collective identity. We propose a framework for tracking linguistic change as it happens and for understanding how specific users react to these evolving norms. By applying this framework to two large online communities we show that users follow a determined two-stage lifecycle with respect to their susceptibility to linguistic change: a linguistically innovative learning phase in which users adopt the language of the community followed by a conservative phase in which users stop changing and the evolving community norms pass them by. Building on this observation, we show how this framework can be used to detect, early in a user's career, how long she will stay active in the community. Thus, this work has practical significance for those who design and maintain online communities. It also yields new theoretical insights into the evolution of linguistic norms and the complex interplay between community-level and individual-level linguistic change.  We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural language models, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track its linguistic displacement over time. We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book Ngrams. Our analysis reveals interesting patterns of language usage change commensurate with each medium. Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics CL community in the social dimension of language. In this article we present a survey of the emerging field of "computational sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction, and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions used in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges. This study examines the JUNIOR SUMMIT online community, which consisted of 3,062 adolescents representing 139 countries, varying SES, and a range of experience with computers. The online forum culminated in the election of 100 delegates. By analyzing the messages posted before results of the election were announced, we explore whether language use predicts who was elected as a leader, as well as gender differences in leadership style. Results indicate that the young online leaders do not adhere to adult leadership styles of contributing many ideas, sticking to task, and using powerful language. On the contrary, while the young people elected as delegates do contribute more, their linguistic style is likely to keep the goals and needs of the group as central-by referring to the group rather than to themselves, and by synthesizing the posts of others rather than solely contributing their own ideas. Furthermore, both boy and girl leaders follow this pattern of interpersonal language use. These results reassure us that young people can be civically engaged and community minded, while indicating that these concepts themselves may change through contact with the next generation. Language change is a complex social phenomenon, revealing pathways of communication and sociocultural influence. But, while language change has long been a topic of study in sociolinguistics, traditional linguistic research methods rely on circumstantial evidence, estimating the direction of change from differences between older and younger speakers. In this paper, we use a data set of several million Twitter users to track language changes in progress. First, we show that language change can be viewed as a form of social influence: we observe complex contagion for phonetic spellings and “netspeak” abbreviations (e.g., lol), but not for older dialect markers from spoken language. Next, we test whether specific types of social network connections are more influential than others, using a parametric Hawkes process model. We find that tie strength plays an important role: densely embedded social ties are significantly better conduits of linguistic influence. Geographic locality appears to play a more limited role: we find relatively little evidence to support the hypothesis that individuals are more influenced by geographically local social ties, even in their usage of geographical dialect markers. Part I. Problems and Methods of Analysis: 1. The study of language in its social context 2. First approach to the structure of New York City English 3. The social stratification of English in New York City department stores 4. The isolation of contextual style 5. The linguistic interview 6. The survey of the lower east side Part II. Social Differentiation: 7. Class differentiation of the variables 8. Further analysis of the variables 9. Distribution of the variables in apparent time 10. Other linguistic variables Part III. Social Evaluation: 11. Subjective evaluation of the variables 12. Self-evaluation and linguistic security 13. General attitudes towards the speech of New York City Part IV. Synthesis: 14. The structure of the New York City vowel system. Variationist sociolinguistics :change, observation, interpretation , Variationist sociolinguistics :change, observation, interpretation , کتابخانه دیجیتال و فن آوری اطلاعات دانشگاه امام صادق(ع) In this paper we present a new computational technique to detect and analyze statistically significant geographic variation in language. While previous approaches have primarily focused on lexical variation between regions, our method identifies words that demonstrate semantic and syntactic variation as well. We extend recently developed techniques for neural language models to learn word representations which capture differing semantics across geographical regions. In order to quantify this variation and ensure robust detection of true regional differences, we formulate a null model to determine whether observed changes are statistically significant. Our method is the first such approach to explicitly account for random variation due to chance while detecting regional variation in word meaning. To validate our model, we study and analyze two different massive online data sets: millions of tweets from Twitter as well as millions of phrases contained in the Google Book Ngrams. Our analysis reveals interesting facets of language change across countries.