Automated Crowdturfing Attacks and Defenses in Online Review Systems

Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper, we identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks or RNNs) to automate the generation of fake online reviews for products and services. Not only are these attacks cheap and therefore more scalable, but they can control rate of content output to eliminate the signature burstiness that makes crowdsourced campaigns easy to detect. Using Yelp reviews as an example platform, we show how a two phased review generation and customization attack can produce reviews that are indistinguishable by state-of-the-art statistical detectors. We conduct a survey-based user study to show these reviews not only evade human detection, but also score high on "usefulness" metrics by users. Finally, we develop novel automated defenses against these attacks, by leveraging the lossy transformation introduced by the RNN training and generation cycle. We consider countermeasures against our mechanisms, show that they produce unattractive cost-benefit tradeoffs for attackers, and that they can be further curtailed by simple constraints imposed by online service providers

Multilingual Cross-domain Perspectives on Online Hate Speech

In this report, we present a study of eight corpora of online hate speech, by demonstrating the NLP techniques that we used to collect and analyze the jihadist, extremist, racist, and sexist content. Analysis of the multilingual corpora shows that the different contexts share certain characteristics in their hateful rhetoric. To expose the main features, we have focused on text classification, text profiling, keyword and collocation extraction, along with manual annotation and qualitative study.Comment: 24 page

Detecting Turkish fake news via text mining to protect brand integrity

Fake news has been in our lives as part of the media for years. With the recent spread of digital news platforms, it affects not only traditional media but also online media as well. Therefore, while companies seek to increase their own brand awareness, they should also protect their brands against fake news spread on social networks and traditional media. This study discusses a solution that accurately classifies the Turkish news published online as real and fake. For this purpose, a machine learning model is trained with tagged news. Initially, the headlines were analyzed within the scope of this study that are collected from Turkish online sources. As a next step, in addition to the headlines of these news, news contexts are also used in the analysis. Analysis are done with unigrams and bigrams. The results show 95% success for the headlines and 80% for the texts for correctly classifying the fake Turkish news articles. This is the first study in the literature that introduces an ML model that can accurately identify fake news in Turkish language

"Advancements in Fake News Detection: A Comparative Study of Machine and Deep Learning Methods"

In the contemporary landscape of information dissemination, the detection of fake news has emerged as a crucial undertaking due to the rapid proliferation of misinformation across various online channels. This study undertakes a comprehensive examination of fake news detection techniques, encompassing both traditional machine learning and advanced deep learning methods. We explore the efficacy of diverse feature extraction methods coupled with supervised learning methods. Through experiments conducted on established benchmark datasets, we assess the performance of these approaches in terms of classification report, while also scrutinizing their computational efficiency and scalability. Our findings offer valuable insights into the strengths and limitations of each method for fake news detection, thereby furnishing researchers and practitioners with guidance for formulating effective strategies to combat misinformation across online media platforms