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ACM Transactions on

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FusE: Entity-Centric Data Fusion on Linked Data

Many current web pages include structured data which can directly be processed and used. Search engines, in particular, gather that structured data and provide question answering capabilities over the integrated data with an entity-centric presentation of the results. Due to the decentralized nature of the web, multiple structured data sources can... (more)

What Web Template Extractor Should I Use? A Benchmarking and Comparison for Five Template Extractors

A Web template is a resource that implements the structure and format of a website, making it ready... (more)

Polarization and Fake News: Early Warning of Potential Misinformation Targets

Users’ polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this article, we introduce a framework for promptly identifying polarizing content on social media and, thus,... (more)

Cashtag Piggybacking: Uncovering Spam and Bot Activity in Stock Microblogs on Twitter

Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never been... (more)

Layout Cross-Platform and Cross-Browser Incompatibilities Detection using Classification of DOM Elements

Web applications can be accessed through a variety of user agent configurations, in which the... (more)

Exploiting Usage to Predict Instantaneous App Popularity: Trend Filters and Retention Rates

Popularity of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings. A problem with these... (more)

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About TWEB

The journal Transactions on the Web (TWEB) publishes refereed articles reporting the results of research on Web content, applications, use, and related enabling technologies.

The scope of TWEB is described on the Call for Papers page. Authors are invited to submit original research papers for consideration by following the directions on the Author Guidelines page.

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Forthcoming Articles
'The Enemy Among Us': Detecting Cyber Hate Speech with Threats-based Othering Language Embeddings

Offensive or antagonistic language targeted at individuals and social groups based on their personal characteristics (also known as cyber hate speech or cyber hate) has been frequently posted and widely circulated via the World Wide Web. This can be considered as a key risk factor for individual and societal tension linked to regional instability. Automated Web-based cyber hate detection is important for observing and understanding community and regional societal tension - especially in online social networks where posts can be rapidly and widely viewed and disseminated. While previous work has involved using lexicons, bags-of-words or probabilistic language parsing approaches, they often suffer from a similar issue which is that cyber hate can be subtle and indirect - thus depending on the occurrence of individual words or phrases can lead to a significant number of false negatives, providing inaccurate representation of the trends in cyber hate. This problem motivated us to challenge thinking around the representation of subtle language use, such as references to perceived threats from the other including immigration or job prosperity in a hateful context. We propose a novel framework that utilises language use around the concept of othering and intergroup threat theory to identify these subtleties and we implement a novel classification method using embedding learning to compute semantic distances between parts of speech considered to be part of an othering narrative. To validate our approach we conduct several experiments on different types of cyber hate, namely religion, disability, race and sexual orientation, with F-measure scores for classifying hateful instances obtained through applying our model of 0.93, 0.86, 0.97 and 0.98 respectively, providing a significant improvement in classifier accuracy over the state-of-the-art.

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