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Diachronic word embeddings—vector representations of words over time—offer remarkable insights into the evolution of language and provide a tool for quantifying sociocultural change from text documents. Prior work has used such embeddings to identify shifts in the meaning of individual words. However, simply knowing that a word has changed in meaning is insufficient to identify the instances of word usage that convey the historical meaning or the newer meaning. In this study, we link diachronic word embeddings to documents, by situating those documents as leaders or laggards with respect to ongoing semantic changes. Specifically, we propose a novel method to quantify the degree of semantic progressiveness in each word usage, and then show how these usages can be aggregated to obtain scores for each document. We analyze two large collections of documents, representing legal opinions and scientific articles. Documents that are scored as semantically progressive receive a larger number of citations, indicating that they are especially influential. Our work thus provides a new technique for identifying lexical semantic leaders and demonstrates a new link between progressive use of language and influence in a citation network. [ABSTRACT FROM AUTHOR]

Language is shaped by the relationships between the speaker/writer and the audience, the object of discussion, and the talk itself. In turn, language is used to reshape these relationships over the course of an interaction. Computational researchers have succeeded in operationalizing sentiment, formality, and politeness, but each of these constructs captures only some aspects of social and relational meaning. Theories of interactional stancetaking have been put forward as holistic accounts, but until now, these theories have been applied only through detailed qualitative analysis of (portions of) a few individual conversations. In this article, we propose a new computational operationalization of interpersonal stancetaking. We begin with annotations of three linked stance dimensions—affect, investment, and alignment—on 68 conversation threads from the online platform Reddit. Using these annotations, we investigate thread structure and linguistic properties of stancetaking in online conversations. We identify lexical features that characterize the extremes along each stancetaking dimension, and show that these stancetaking properties can be predicted with moderate accuracy from bag-of-words features, even with a relatively small labeled training set. These quantitative analyses are supplemented by extensive qualitative analysis, highlighting the compatibility of computational and qualitative methods in synthesizing evidence about the creation of interactional meaning. [ABSTRACT FROM AUTHOR]

Quantifying the degree of spatial dependence for linguistic variables is a key task for analyzing dialectal variation. However, existing approaches have important drawbacks. First, they are based on parametric models of dependence, which limits their power in cases where the underlying parametric assumptions are violated. Second, they are not applicable to all types of linguistic data: Some approaches apply only to frequencies, others to boolean indicators of whether a linguistic variable is present. We present a new method for measuring geographical language variation, which solves both of these problems. Our approach builds on Reproducing Kernel Hilbert Space (RKHS) representations for nonparametric statistics, and takes the form of a test statistic that is computed from pairs of individual geotagged observations without aggregation into predefined geographical bins. We compare this test with prior work using synthetic data as well as a diverse set of real data sets: a corpus of Dutch tweets, a Dutch syntactic atlas, and a data set of letters to the editor in North American newspapers. Our proposed test is shown to support robust inferences across a broad range of scenarios and types of data. [ABSTRACT FROM AUTHOR]

How do humanities scholars make sense of new or otherwise unfamiliar archives? Is there a role for computational text analysis in the process of sensemaking? We propose that topic modeling, when conceived as a process of thematic exploration, can provide a new entry point into this process. To this end, we present research on a new software tool called TOME: Interactive TOpic Model and MEtadata Visualization, designed to support the exploratory thematic analysis of digitized archival collections. TOME is centered around a set of visualizations intended to facilitate the interpretation of the topic model and its incorporation into extant humanities research practices. In contrast to other topic model browsers, which present the model on its own terms, ours is informed by the process of conducting early-stage humanities research. Our article thus also demonstrates the conceptual conversions--in terms of both design and process--that interdisciplinary collaboration necessarily entails. In making these conversions explicit, and exploring the implications of their successes and failures, we take up the call, as voiced by Johanna Drucker (Humanities approaches to graphical display. Digital Humanities Quarterly, 5(1), 2011), to resist the 'intellectual Trojan horse' of visualization. We seek to model a new mode of interdisciplinary inquiry, one that brings the methodological emphasis of the digital humanities to bear on the practices of humanities research and computer science alike. [ABSTRACT FROM AUTHOR]

Stylistic variation in online social media writing is well attested: for example, geographical analysis of the social media service Twitter has replicated isoglosses for many known lexical variables from speech, while simultaneously revealing a wealth of new geographical lexical variables, including emoticons, phonetic spellings, and phrasal abbreviations. However, less is known about the social role of variation in online writing. This article examines online writing variation in the context of audience design, focusing on affordances offered by Twitter that allow users to modulate a message's intended audience. We find that the frequency of nonstandard lexical variables is inversely related to the size of the intended audience: as writers target smaller audiences, the frequency of lexical variables increases. In addition, these variables are more often used in messages that are addressed to individuals who are known to be geographically local. This phenomenon holds not only for geographically differentiated lexical variables, but also for nonstandard variables that are widely used throughout the United States. These findings suggest that users of social media are attuned to both the nature of their audience and the social meaning of lexical variation and that they customize their self-presentation accordingly. [ABSTRACT FROM AUTHOR]

Social media features a wide range of non-standard spellings, many of which appear inspired by phonological variation. However, the nature of the connection between variation across the spoken and written modalities remains poorly understood. Are phonological variables transferred to writing on the level of graphemes, or is the larger system of contextual patterning also transferred? This paper considers orthographic coda deletions corresponding to the phonological variables of (ing) and (t,d). In both cases, orthography mirrors speech: reduction of the -ing suffix depends on the word's syntactic category, and reduction of the -t,-d suffix depends on the succeeding phonological context. These spellings are more frequently used in informal conversational contexts, and in areas with high proportions of African Americans, again mirroring the patterning of the associated phonological variables. This suggests a deep connection between variation in the two modalities, necessitating a new account of the production of cross-modal variation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging. The central assumption of our work is that by combining cues from multiple languages, the structure of each becomes more apparent. We consider two ways of applying this intuition to the problem of unsupervised part-of-speech tagging: a model that directly merges tag structures for a pair of languages into a single sequence and a second model which instead incorporates multilingual context using latent variables. Both approaches are formulated as hierarchical Bayesian models, using Markov Chain Monte Carlo sampling techniques for inference. Our results demonstrate that by incorporating multilingual evidence we can achieve impressive performance gains across a range of scenarios. We also found that performance improves steadily as the number of available languages increases. [ABSTRACT FROM AUTHOR]

This paper presents a new method for inferring the semantic properties of documents by leveraging free-text keyphrase annotations. Such annotations are becoming increasingly abundant due to the recent dramatic growth in semi-structured, user-generated online content. One especially relevant domain is product reviews, which are often annotated by their authors with pros/cons keyphrases such as "a real bargain" or "good value." These annotations are representative of the underlying semantic properties; however, unlike expert annotations, they are noisy: lay authors may use different labels to denote the same property, and some labels may be missing. To learn using such noisy annotations, we find a hidden paraphrase structure which clusters the keyphrases. The paraphrase structure is linked with a latent topic model of the review texts, enabling the system to predict the properties of unannotated documents and to effectively aggregate the semantic properties of multiple reviews. Our approach is implemented as a hierarchical Bayesian model with joint inference. We find that joint inference increases the robustness of the keyphrase clustering and encourages the latent topics to correlate with semantically meaningful properties. Multiple evaluations demonstrate that our model substantially outperforms alternative approaches for summarizing single and multiple documents into a set of semantically salient keyphrases. [ABSTRACT FROM AUTHOR]

Gesture is a non-verbal modality that can contribute crucial information to the understanding of natural language. But not all gestures are informative, and non-communicative hand motions may confuse natural language processing (NLP) and impede learning. People have little difficulty ignoring irrelevant hand movements and focusing on meaningful gestures, suggesting that an automatic system could also be trained to perform this task. However, the informativeness of a gesture is context-dependent and labeling enough data to cover all cases would be expensive. We present conditional modality fusion, a conditional hidden-variable model that learns to predict which gestures are salient for coreference resolution, the task of determining whether two noun phrases refer to the same semantic entity. Moreover, our approach uses only coreference annotations, and not annotations of gesture salience itself. We show that gesture features improve performance on coreference resolution, and that by attending only to gestures that are salient, our method achieves further significant gains. In addition, we show that the model of gesture salience learned in the context of coreference accords with human intuition, by demonstrating that gestures judged to be salient by our model can be used successfully to create multimedia keyframe summaries of video. These summaries are similar to those created by human raters, and significantly outperform summaries produced by baselines from the literature. [ABSTRACT FROM AUTHOR]