Social Media

AbstractTextual entailment has been recognized as a generic task that captures major semantic inference needs across many natural language processing applications. However, to date, textual entailment has not been considered …...

The variety of engaging interactions among users in social medial distinguishes it from traditional Web media. Such a fea- ture should be utilized while attempting to provide intelligent services to …...

We investigate whether wording, stylistic choices, and online behavior can be used to predict the age category of blog authors. Our hypothesis is that significant changes in writing style distinguish …...

This paper offers an approach for governments to harness the information contained in social media in order to make public inspections and disclosure more efficient. As a case study, we …...

AbstractWe present a probabilistic language model that captures temporal dynamics and conditions on arbitrary non-linguistic context features. These context features serve as important indicators of language changes that are otherwise …...

AbstractEarly detection of suicidal thoughts is an important part of effective suicide prevention. Such thoughts may be expressed online, especially by young people. This paper presents on-going work on the …...

Social media represents a rich source of up- to-date information about events such as in- cidents. The sheer amount of available infor- mation makes machine learning approaches a necessity for …...

Organisations — including insurgent movements — harness social media to engage potential consumers. They evoke sympathetic (and antipathic) response; content sharing engenders affinity and community. We report on a pilot …...

AbstractBecause of the increasing popularity of social media, much information has been shared on the internet, enabling social media users to understand various real world events. Particularly, social media-based infectious …...

Based on the assumption that comment with positive sentimental polarity to a negative issue has high probability to be a sarcasm, we propose a simple yet efficient method to collect …...

AbstractTextual information is of critical importance for automatic user classification in social media. However, most previous studies model textual features in a single perspective while the text in a user …...

AbstractMost neural network models for document classification on social media focus on text information to the neglect of other information on these platforms. In this paper, we classify post stance …...

Under the issue of gender and Natural Language Processing (NLP), most papers aim at gender- norm language that spoken by biologically males and females with opposite-sex desires. However, from the …...

Sentiment classification techniques have been widely used for analyzing user opinions. In conventional supervised learning methods, hand-crafted features are needed, which requires a thorough understanding of the domain. Since social …...

AbstractThe rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of hateful and offensive language on the Internet. Previous research suggests …...

AbstractTraditional topic models are insufficient for topic extraction in social media. The existing methods only consider text information or simultaneously model the posts and the static characteristics of social media. …...

AbstractStance detection in social media is a well-studied task in a variety of domains. Nevertheless, previous work has mostly focused on multiclass versions of the problem, where the labels are …...

AbstractTransfer learning and domain adaptive learning have been applied to various fields including computer vision (e.g., image recognition) and natural language processing (e.g., text classification). One of the benefits of …...

AbstractWith the growth in the usage of social media, it has become increasingly common for people to hide behind a mask and abuse others. We have attempted to detect such …...

AbstractExisting studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, health-care workers require …...

AbstractIn this paper we analyze the use of modal verbs in a corpus of texts related to the vaccination debate. Broadly speaking, the vaccination debate centers around whether vaccination is …...

AbstractReading comprehension (RC) on social media such as Twitter is a critical and challenging task due to its noisy, informal, but informative nature. Most existing RC models are developed on …...

AbstractSocial media is a valuable data resource for various natural language processing (NLP) tasks. However, standard NLP tools were often designed with standard texts in mind, and their performance decreases …...

AbstractHate speech and toxic comments are a common concern of social media platform users. Although these comments are, fortunately, the minority in these platforms, they are still capable of causing …...

AbstractConventional opinion polls were usually conducted via questionnaires or phone interviews, which are time-consuming and error-prone. With the advances in social networking platforms, it’s easier for the general public to …...

With the increasing importance of social me- dia in everyone’s life, the risk of its misuse by criminals is also increasing. In particular children are at risk of becoming victims …...

AbstractFor manufacturers of home appliances, the Studying discussion of products on social media can help manufacturers improve their products. Opinions provided through online reviews can immediately reflect whether the product …...

AbstractOffensive language detection and analysis has become a major area of research in Natural Language Processing. The freedom of participation in social media has exposed online users to posts designed …...

AbstractSifting French Tweets to Investigate the Impact of Covid-19 in Triggering Intense Anxiety. Social media can be leveraged to understand public sentiment and feelings in real-time, and target public health …...

AbstractThis survey builds an interdisciplinary picture of Argument Mining (AM), with a strong focus on its potential to address issues related to Social and Political Science. More specifically, we focus …...

AbstractDetecting rumors on social media is a very critical task with significant implications to the economy, public health, etc. Previous works generally capture effective features from texts and the propagation …...

AbstractSocial media is the ultimate challenge for many natural language processing tools. The constant emergence of linguistic constructs challenge even the most sophisticated NLP tools. Predicting word embeddings for out …...

AbstractIn multilingual colloquial settings, it is a habitual occurrence to compose expressions of text or speech containing tokens or phrases of different languages, a phenomenon popularly known as code-switching or …...

AbstractSocial media often serves as a breeding ground for various hateful and offensive content. Identifying such content on social media is crucial due to its impact on the race, gender, …...

AbstractThe study presented in this paper demonstrates how transcribed podcast material differs with respect to lexical content from other collections of English language data: editorial text, social media, both long …...

AbstractIn this work, we explore the relationship between depression and manifestations of happiness in social media. While the majority of works surrounding depression focus on symptoms, psychological research shows that …...

AbstractDuring natural disasters, people often use social media platforms, such as Twitter, to post information about casualties and damage produced by disasters. This information can help relief authorities gain situational …...

AbstractThe prevalent use of offensive content in social media has become an important reason for concern for online platforms (customer service chat-boxes, social media platforms, etc). Classifying offensive and hate-speech …...

AbstractEasy access, variety of content, and fast widespread interactions are some of the reasons making social media increasingly popular. However, this rise has also enabled the propagation of fake news, …...

AbstractFake news detection is a challenging problem due to its tremendous real-world political and social impacts. Recent fake news detection works focus on learning news features from News Propagation Graph …...

AbstractSentiment analysis in social media is challenging since posts are short of context. As a popular way to express emotion on social media, stickers related to these posts can supplement …...

Abstract“Multimodal Named Entity Recognition (MNER) is a challenging task in social mediadue to the combination of text and image features. Previous MNER work has focused onpredicting entity information after fusing …...

AbstractPropaganda aims to persuade an audience by appealing to emotions and using faulty reasoning, with the purpose of promoting a particular point of view. Similarly, metaphor modifies the semantic frame, …...

AbstractThis paper presents an approach for training lightweight and robust language models for Bulgarian that mitigate gender, political, racial, and other biases in the data. Our method involves scraping content …...