@article{Setty2020,

title = {Truth be told: Fake news detection using user reactions on reddit},

author = {Vinay Setty and Erlend Rekve},

url = {https://dl.acm.org/doi/pdf/10.1145/3340531.3417463},

doi = {https://doi.org/10.1145/3340531.3417463},

year  = {2020},

date = {2020-10-01},

journal = {Proceedings of the 29th acm international conference on information knowledge management},

pages = {3325–3328},

abstract = {In this paper, we provide a large dataset for fake news detection using social media comments. The dataset consists of 12,597 claims (of which 63% are labelled as fake) from four different sources (Snopes, Poltifact, Emergent and Twitter). The novel part of the dataset is that it also includes over 662K social media discussion comments related to these claims from Reddit. We make this dataset public for the research community. In addition, for the task of fake news detection using social media comments, we provide a simple but strong baseline solution deep neural network model which beats several solutions in the literature.},

note = {Pre SFI},

keywords = {Deep neural networks, Fake news detection, Reddit comments, WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {article}

}

@article{Botnevik2020,

title = {Brenda: Browser extension for fake news detection},

author = {Bjarte Botnevik and Eirik Sakariassen and Vinay Setty},

url = {https://arxiv.org/pdf/2005.13270.pdf},

doi = {10.1145/3397271.3401396},

year  = {2020},

date = {2020-05-27},

journal = {Proceedings of the 43rd international acm sigir conference on research and development in information retrieval},

pages = { 2117–2120},

publisher = {Association for Computing Machinery},

abstract = {Misinformation such as fake news has drawn a lot of attention in recent years. It has serious consequences on society, politics and economy. This has lead to a rise of manually fact-checking websites such as Snopes and Politifact. However, the scale of misinformation limits their ability for verification. In this demonstration, we propose BRENDA a browser extension which can be used to automate the entire process of credibility assessments of false claims. Behind the scenes BRENDA uses a tested deep neural network architecture to automatically identify fact check worthy claims and classifies as well as presents the result along with evidence to the user. Since BRENDA is a browser extension, it facilities fast automated fact checking for the end user without having to leave the Webpage.},

note = {Pre SFI},

keywords = {Fake news detection, Hierarchical attention, Neural networks, WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {article}

}