Related papers: News Headlines Dataset For Sarcasm Detection

We investigate the impact of using author context on textual sarcasm detection. We define author context as the embedded representation of their historical posts on Twitter and suggest neural models that extract these representations. We…

News articles covering policy issues are an essential source of information in the social sciences and are also frequently used for other use cases, e.g., to train NLP language models. To derive meaningful insights from the analysis of…

The context-dependent nature of online aggression makes annotating large collections of data extremely difficult. Previously studied datasets in abusive language detection have been insufficient in size to efficiently train deep learning…

In recent years, the problem of misinformation on the web has become widespread across languages, countries, and various social media platforms. Although there has been much work on automated fake news detection, the role of images and…

With social media datasets being increasingly shared by researchers, it also presents the caveat that those datasets are not always completely replicable. Having to adhere to requirements of platforms like Twitter, researchers cannot…

Since state-of-the-art approaches to offensive language detection rely on supervised learning, it is crucial to quickly adapt them to the continuously evolving scenario of social media. While several approaches have been proposed to tackle…

Online hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms. This phenomenon is primarily fostered by…

Despite the extensive communication benefits offered by social media platforms, numerous challenges must be addressed to ensure user safety. One of the most significant risks faced by users on these platforms is targeted hate speech. Social…

Social media has become a popular means for people to consume news. Meanwhile, it also enables the wide dissemination of fake news, i.e., news with intentionally false information, which brings significant negative effects to the society.…

Humans like to express their opinions and crave the opinions of others. Mining and detecting opinions from various sources are beneficial to individuals, organisations, and even governments. One such organisation is news media, where a…

A key challenge for automatic hate-speech detection on social media is the separation of hate speech from other instances of offensive language. Lexical detection methods tend to have low precision because they classify all messages…

In the past decade, sarcasm detection has been intensively conducted in a textual scenario. With the popularization of video communication, the analysis in multi-modal scenarios has received much attention in recent years. Therefore,…

Clickbait (headlines) make use of misleading titles that hide critical information from or exaggerate the content on the landing target pages to entice clicks. As clickbaits often use eye-catching wording to attract viewers, target contents…

We present an approach to detect fake news in Twitter at the account level using a neural recurrent model and a variety of different semantic and stylistic features. Our method extracts a set of features from the timelines of news Twitter…

The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of abusive and offensive language on the Internet. Previous research suggests that such hateful content tends to come from…

This study investigates echo chambers in social networks through an analysis of Twitter news accounts. Utilizing bias labels from the AllSides website, we construct a dataset representing six dimensions of news bias. Through manual…

One of the major sources of trending news, events and opinion in the current age is micro blogging. Twitter, being one of them, is extensively used to mine data about public responses and event updates. This paper intends to propose methods…

Social media is awash with hateful content, much of which is often veiled with linguistic and topical diversity. The benchmark datasets used for hate speech detection do not account for such divagation as they are predominantly compiled…

With the proliferation of social media, many studies resort to social media to construct datasets for developing social meaning understanding systems. For the popular case of Twitter, most researchers distribute tweet IDs without the actual…

With the increased use of social media platforms by people across the world, many new interesting NLP problems have come into existence. One such being the detection of sarcasm in the social media texts. We present a corpus of tweets for…