Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach
Department of Computer Science, Stony Brook University（石溪大学计算机科学系）；Melbourne Graduate School of Education, The University of Melbourne（墨尔本大学墨尔本教育研究生院）；Judge Business School, University of Cambridge（剑桥大学贾吉商学院）；Graduate School of Business, Stanford University（斯坦福大学商学院）；Columbia Business School, Columbia University（哥伦比亚大学哥伦比亚商学院）；Computer and Information Science, University of Pennsylvania（宾夕法尼亚大学计算机与信息科学系）
Vivek Kulkarni, Margaret L. Kern, David Stillwell, Michal Kosinski, Sandra Matz, Lyle Ungar, Steven Skiena, H. Andrew Schwartz
Over the past century, personality theory and research has successfully identified core sets of characteristics that consistently describe and explain fundamental differences in the way people think, feel and behave. Such characteristics were derived through theory, dictionary analyses, and survey research using explicit self-reports. The availability of social media data spanning millions of users now makes it possible to automatically derive characteristics from language use -- at large scale. Taking advantage of linguistic information available through Facebook, we study the process of inferring a new set of potential human traits based on unprompted language use. We subject these new traits to a comprehensive set of evaluations and compare them with a popular five factor model of personality. We find that our language-based trait construct is often more generalizable in that it often predicts non-questionnaire-based outcomes better than questionnaire-based traits (e.g. entities someone likes, income and intelligence quotient), while the factors remain nearly as stable as traditional factors. Our approach suggests a value in new constructs of personality derived from everyday human language use.
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
Given the complexity of human minds and their behavioral flexibility, it requires sophisticated data analysis to sift through a large amount of human behavioral evidence to model human minds and to predict human behavior. People currently spend a significant amount of time on social media such as Twitter and Facebook. Thus many aspects of their lives and behaviors have been digitally captured and continuously archived on these platforms. This makes social media a great source of large, rich and diverse human behavioral evidence. In this paper, we survey the recent work on applying machine lear…
Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives
School of Computer Science and Technology, Dalian University of Technology, Dalian, China（计算机科学与技术学院，大连理工大学，大连，中国）；Criminal Investigation Police University of China, Shenyang, China（中国刑事侦查警察大学，沈阳，中国）；School of Foreign Languages, Dalian University of Technology, Dalian, China（外语学院，大连理工大学，大连，中国）
Identifying controversial topics is not only interesting from a social point of view, it also enables the application of methods to avoid the information segregation, creating better discussion contexts and reaching agreements in the best cases. In this paper we develop a systematic method for controversy detection based primarily on the jargon used by the communities in social media. Our method dispenses with the use of domain-specific knowledge, is language-agnostic, efficient and easy to apply. We perform an extensive set of experiments across many languages, regions and contexts, taking co…
Text from social media provides a set of challenges that can cause traditional NLP approaches to fail. Informal language, spelling errors, abbreviations, and special characters are all commonplace in these posts, leading to a prohibitively large vocabulary size for word-level approaches. We propose a character composition model, tweet2vec, which finds vector-space representations of whole tweets by learning complex, non-local dependencies in character sequences. The proposed model outperforms a word-level baseline at predicting user-annotated hashtags associated with the posts, doing significa…
Language Technologies Research Centre（语言技术研究中心）；KCIS, IIIT Hyderabad（海得拉巴国际信息技术研究所知识计算与集成系统中心）
The phenomenon of mixing the vocabulary and syntax of multiple languages within the same utterance is called Code-Mixing. This is more evident in multilingual societies. In this paper, we have developed a system for SemEval 2020: Task 9 on Sentiment Analysis for Code-Mixed Social Media Text. Our system first generates two types of embeddings for the social media text. In those, the first one is character level embeddings to encode the character level information and to handle the out-of-vocabulary entries and the second one is FastText word embeddings for capturing morphology and semantics. Th…
Language in social media is mostly driven by new words and spellings that are constantly entering the lexicon thereby polluting it and resulting in high deviation from the formal written version. The primary entities of such language are the out-of-vocabulary (OOV) words. In this paper, we study various sociolinguistic properties of the OOV words and propose a classification model to categorize them into at least six categories. We achieve 81.26% accuracy with high precision and recall. We observe that the content features are the most discriminative ones followed by lexical and context featur…
In recent years, numerous studies have inferred personality and other traits from people's online writing. While these studies are encouraging, more information is needed in order to use these techniques with confidence. How do linguistic features vary across different online media, and how much text is required to have a representative sample for a person? In this paper, we examine several large sets of online, user-generated text, drawn from Twitter, email, blogs, and online discussion forums. We examine and compare population-wide results for the linguistic measure LIWC, and the inferred tr…
A Chinese Multi-label Affective Computing Dataset Based on Social Media Network Users
Beijing Institute of Technology（北京理工大学）；Institute of Scientific and Technical Research on Archives（档案科学技术研究所）；China Electronics Engineering Design Institute Co., Ltd.（中国电子工程设计院有限公司）
Cognitive and social traits of individuals are reflected in language use. Moreover, individuals who are prone to spread fake news online often share common traits. Building on these ideas, we introduce a model that creates representations of individuals on social media based only on the language they produce, and use them to detect fake news. We show that language-based user representations are beneficial for this task. We also present an extended analysis of the language of fake news spreaders, showing that its main features are mostly domain independent and consistent across two English data…
Università della Svizzera italiana（瑞士意大利语大学）；University of Amsterdam（阿姆斯特丹大学）
Due to the worldwide accessibility to the Internet along with the continuous advances in mobile technologies, physical and digital worlds have become completely blended, and the proliferation of social media platforms has taken a leading role over this evolution. In this paper, we undertake a thorough analysis towards better visualising and understanding the factors that characterise and differentiate social media users affected by mental disorders. We perform different experiments studying multiple dimensions of language, including vocabulary uniqueness, word usage, linguistic style, psychome…
ViTOED: A Dataset for Target-Oriented Emotion Detection on Vietnamese Social Media Texts
Faculty of Information Science and Engineering, University of Information Technology（信息科学与工程学院，信息科技大学）；Vietnam National University, Ho Chi Minh City（越南国家大学胡志明市分校）
Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. To sense the whys of certain social user's demands and cultural-driven interests, however, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited since this process has been typically been text-based. Following this trend on visual-based social analysis, we present a novel methodology based on Deep Learning to build a combined image-and-text…
Virginia Tech（弗吉尼亚理工大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Traditional disease surveillance can be augmented with a wide variety of real-time sources such as, news and social media. However, these sources are in general unstructured and, construction of surveillance tools such as taxonomical correlations and trace mapping involves considerable human supervision. In this paper, we motivate a disease vocabulary driven word2vec model (Dis2Vec) to model diseases and constituent attributes as word embeddings from the HealthMap news corpus. We use these word embeddings to automatically create disease taxonomies and evaluate our model against corresponding h…
People's personality and motivations are manifest in their everyday language usage. With the emergence of social media, ample examples of such usage are procurable. In this paper, we aim to analyze the vocabulary used by close to 200,000 Blogger users in the U.S. with the purpose of geographically portraying various demographic, linguistic, and psychological dimensions at the state level. We give a description of a web-based tool for viewing maps that depict various characteristics of the social media users as derived from this large blog dataset of over two billion words.
University of Stuttgart（斯图加特大学）；Leibniz-Institut für Wissensmedien（莱布尼茨知识媒体研究所）；University of Tübingen（蒂宾根大学）
Machine-learned models for author profiling in social media often rely on data acquired via self-reporting-based psychometric tests (questionnaires) filled out by social media users. This is an expensive but accurate data collection strategy. Another, less costly alternative, which leads to potentially more noisy and biased data, is to rely on labels inferred from publicly available information in the profiles of the users, for instance self-reported diagnoses or test results. In this paper, we explore a third strategy, namely to directly use a corpus of items from validated psychometric tests…
We explore linguistic and behavioral features of dogmatism in social media and construct statistical models that can identify dogmatic comments. Our model is based on a corpus of Reddit posts, collected across a diverse set of conversational topics and annotated via paid crowdsourcing. We operationalize key aspects of dogmatism described by existing psychology theories (such as over-confidence), finding they have predictive power. We also find evidence for new signals of dogmatism, such as the tendency of dogmatic posts to refrain from signaling cognitive processes. When we use our predictive …
A Proactive Multi-Agent Dialogue Framework for Assessing Social Language Disorder Traits in Autism
Department of Computer Science（计算机科学系）；University at Albany（阿尔巴尼大学）；Department of Management Information System（管理信息系统系）；West Virginia University（西弗吉尼亚大学）；Department of Radiology（放射学系）；Washington University in St. Louis（圣路易斯华盛顿大学）；Humanities and Social Sciences（人文学与社会科学）
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；Leiden University（莱顿大学）；Pontifícia Universidade Católica de Minas Gerais（米纳斯吉拉斯天主教大学）；Harvard University（哈佛大学）
WhatsApp is, as of 2018, a significant component of the global information and communication infrastructure, especially in developing countries. However, probably due to its strong end-to-end encryption, WhatsApp became an attractive place for the dissemination of misinformation, extremism and other forms of undesirable behavior. In this paper, we investigate the public perception of WhatsApp through the lens of media. We analyze two large datasets of news and show the kind of content that is being associated with WhatsApp in different regions of the world and over time. Our analyses include t…
PRISM: A Personality-Driven Multi-Agent Framework for Social Media Simulation
University of Liverpool（利物浦大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University College London（伦敦大学学院）；Xi'an Jiaotong-Liverpool University（西安交通大学-利物浦大学）；Chinese Academy of Sciences（中国科学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model
Beijing Institute of Technology（北京理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Comments on social media are very diverse, in terms of content, style and vocabulary, which make generating comments much more challenging than other existing natural language generation~(NLG) tasks. Besides, since different user has different expression habits, it is necessary to take the user's profile into consideration when generating comments. In this paper, we introduce the task of automatic generation of personalized comment~(AGPC) for social media. Based on tens of thousands of users' real comments and corresponding user profiles on weibo, we propose Personalized Comment Generation Net…
Zipf's law defines an inverse proportion between a word's ranking in a given corpus and its frequency in it, roughly dividing the vocabulary into frequent words and infrequent ones. Here, we stipulate that within a domain an author's signature can be derived from, in loose terms, the author's missing popular words and frequently used infrequent-words. We devise a method, termed Latent Personal Analysis (LPA), for finding domain-based attributes for entities in a domain: their distance from the domain and their signature, which determines how they most differ from a domain. We identify the most…
Thapar Institute of Engineering & Technology（塔帕尔工程技术学院）
Amid lockdown period more people express their feelings over social media platforms due to closed third-place and academic researchers have witnessed strong associations between the mental healthcare and social media posts. The stress for a brief period may lead to clinical depressions and the long-lasting traits of prevailing depressions can be life threatening with suicidal ideation as the possible outcome. The increasing concern towards the rise in number of suicide cases is because it is one of the leading cause of premature but preventable death. Recent studies have shown that mining soci…
We evaluate a simple approach to improving zero-shot multilingual transfer of mBERT on social media corpus by adding a pretraining task called translation pair prediction (TPP), which predicts whether a pair of cross-lingual texts are a valid translation. Our approach assumes access to translations (exact or approximate) between source-target language pairs, where we fine-tune a model on source language task data and evaluate the model in the target language. In particular, we focus on language pairs where transfer learning is difficult for mBERT: those where source and target languages are di…
Standard image captioning tasks such as COCO and Flickr30k are factual, neutral in tone and (to a human) state the obvious (e.g., "a man playing a guitar"). While such tasks are useful to verify that a machine understands the content of an image, they are not engaging to humans as captions. With this in mind we define a new task, Personality-Captions, where the goal is to be as engaging to humans as possible by incorporating controllable style and personality traits. We collect and release a large dataset of 201,858 of such captions conditioned over 215 possible traits. We build models that co…
We describe a large vocabulary speech recognition system that is accurate, has low latency, and yet has a small enough memory and computational footprint to run faster than real-time on a Nexus 5 Android smartphone. We employ a quantized Long Short-Term Memory (LSTM) acoustic model trained with connectionist temporal classification (CTC) to directly predict phoneme targets, and further reduce its memory footprint using an SVD-based compression scheme. Additionally, we minimize our memory footprint by using a single language model for both dictation and voice command domains, constructed using …
