There has been a long standing interest in understanding `Social Influence' both in Social Sciences and in Computational Linguistics. In this paper, we present a novel approach to study and measure interpersonal influence in daily interactions. Motivated by the basic principles of influence, we attempt to identify indicative linguistic features of the posts in an online knitting community. We present the scheme used to operationalize and label the posts with indicator features. Experiments with the identified features show an improvement in the classification accuracy of influence by 3.15%. Our results illustrate the important correlation between the characteristics of the language and its potential to influence others.
Linguistic Distancing on Social Media: Indicators of Emotion Regulation Across Age Groups
Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）；Department of Computing Science, University of Alberta（阿尔伯塔大学计算机科学系）；National Research Council Canada（加拿大国家研究委员会）；Department of Psychology, University of Alberta（阿尔伯塔大学心理学系）
Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers
University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔机器智能研究所）；LMU Munich（慕尼黑大学）；Carleton University（卡尔顿大学）；National Research Council Canada（加拿大国家研究委员会）
INESC-ID（里斯本系统与计算机工程研究所）；Universidade de Lisboa（里斯本大学）；ISCTE-IUL（里斯本大学学院）
Discourse markers are universal linguistic events subject to language variation. Although an extensive literature has already reported language specific traits of these events, little has been said on their cross-language behavior and on building an inventory of multilingual lexica of discourse markers. This work describes new methods and approaches for the description, classification, and annotation of discourse markers in the specific domain of the Europarl corpus. The study of discourse markers in the context of translation is crucial due to the idiomatic nature of these structures. Multili…
Linguistic markers of personality traits have been studied extensively, but few cross-cultural studies exist. In this paper, we evaluate how native speakers of American English and Arabic perceive personality traits and naturalness of English utterances that vary along the dimensions of verbosity, hedging, lexical and syntactic alignment, and formality. The utterances are the turns within dialogue fragments that are presented as text transcripts to the workers of Amazon's Mechanical Turk. The results of the study suggest that all four dimensions can be used as linguistic markers of all persona…
CARE-SD: Classifier-based analysis for recognizing and eliminating stigmatizing and doubt marker labels in electronic health records: model development and validation
Emory University（埃默里大学）；University of Florida（佛罗里达大学）；Children’s Healthcare of Atlanta（亚特兰大儿童医疗中心）；Mount Sinai（西奈山医院）；Georgia Institute of Technology（佐治亚理工学院）
University of California, Santa Barbara（加州大学圣巴巴拉分校）
While social media empowers freedom of expression and individual voices, it also enables anti-social behavior, online harassment, cyberbullying, and hate speech. In this paper, we deepen our understanding of online hate speech by focusing on a largely neglected but crucial aspect of hate speech -- its target: either "directed" towards a specific person or entity, or "generalized" towards a group of people sharing a common protected characteristic. We perform the first linguistic and psycholinguistic analysis of these two forms of hate speech and reveal the presence of interesting markers that …
Application of CARE-SD text classifier tools to assess distribution of stigmatizing and doubt-marking language features in EHR
Kaiser Permanente School of Medicine（凯撒永久医学院）；Mount Sinai（西奈山医学院）；Emory College of Arts and Sciences, Emory University（埃默里大学艺术与科学学院）；Children’s Healthcare of Atlanta（亚特兰大儿童医疗中心）；Rollins School of Public Health, Emory University（埃默里大学罗林斯公共卫生学院）；School of Medicine, Emory University（埃默里大学医学院）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
University of East London（东伦敦大学）；Intelligent Voice Ltd.（智能语音有限公司）；Strenuus Ltd.（Strenuus有限公司）
Detecting the elements of deception in a conversation is one of the most challenging problems for the AI community. It becomes even more difficult to design a transparent system, which is fully explainable and satisfies the need for financial and legal services to be deployed. This paper presents an approach for fraud detection in transcribed telephone conversations using linguistic features. The proposed approach exploits the syntactic and semantic information of the transcription to extract both the linguistic markers and the sentiment of the customer's response. We demonstrate the results o…
Disagreements are pervasive in human communication. In this paper we investigate what makes disagreement constructive. To this end, we construct WikiDisputes, a corpus of 7 425 Wikipedia Talk page conversations that contain content disputes, and define the task of predicting whether disagreements will be escalated to mediation by a moderator. We evaluate feature-based models with linguistic markers from previous work, and demonstrate that their performance is improved by using features that capture changes in linguistic markers throughout the conversations, as opposed to averaged values. We de…
Georgia Institute of Technology（佐治亚理工学院）；Columbia University（哥伦比亚大学）；Microsoft Research（微软研究院）
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 "nets…
University of Antioquia（安蒂奥基亚大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
The interest in demographic information retrieval based on text data has increased in the research community because applications have shown success in different sectors such as security, marketing, heath-care, and others. Recognition and identification of demographic traits such as gender, age, location, or personality based on text data can help to improve different marketing strategies. For instance it makes it possible to segment and to personalize offers, thus products and services are exposed to the group of greatest interest. This type of technology has been discussed widely in document…
University of Notre Dame（圣母大学）；Western Washington University（西华盛顿大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
This paper presents the results and conclusions of our participation in the Clickbait Challenge 2017 on automatic clickbait detection in social media. We first describe linguistically-infused neural network models and identify informative representations to predict the level of clickbaiting present in Twitter posts. Our models allow to answer the question not only whether a post is a clickbait or not, but to what extent it is a clickbait post e.g., not at all, slightly, considerably, or heavily clickbaity using a score ranging from 0 to 1. We evaluate the predictive power of models trained on …
LMU Munich（慕尼黑大学）；Bloomberg（彭博）；University of Sheffield（谢菲尔德大学）
Parody is a figurative device used to imitate an entity for comedic or critical purposes and represents a widespread phenomenon in social media through many popular parody accounts. In this paper, we present the first computational study of parody. We introduce a new publicly available data set of tweets from real politicians and their corresponding parody accounts. We run a battery of supervised machine learning models for automatically detecting parody tweets with an emphasis on robustness by testing on tweets from accounts unseen in training, across different genders and across countries. O…
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 reflect…
LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data
Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）；University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校）；University of Tehran（德黑兰大学）；Columbia University（哥伦比亚大学）；University of Florida（佛罗里达大学）
University of Chicago（芝加哥大学）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）
We present an interpretable neural network approach to predicting and understanding politeness in natural language requests. Our models are based on simple convolutional neural networks directly on raw text, avoiding any manual identification of complex sentiment or syntactic features, while performing better than such feature-based models from previous work. More importantly, we use the challenging task of politeness prediction as a testbed to next present a much-needed understanding of what these successful networks are actually learning. For this, we present several network visualizations b…
