|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public.
Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim and has become a key component in applications like fake news detection, claim validation, and argument search.
In this paper, we show that Transfer Learning (TL) can be used to train robust fake news classifiers from little data, achieving 91% accuracy on a fake news dataset in the low-resourced Filipino language, reducing the error by 14% compared to established few-shot baselines.
In fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e. g., snopes. com and politifact. com) and automatic detection systems have been developed in recent years.
The proliferation of fake news and its propagation on social media have become a major concern due to its ability to create devastating impacts.
To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities.
The detector is based almost exclusively on text-based features taken from previous work on clickbait detection, our own work on fake post detection, and features we designed specifically for the challenge.
One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing.