In this paper, we present our submission for SemEval-2019 Task 4: Hyperpartisan News Detection. Hyperpartisan news articles are sharply polarized and extremely biased (onesided). It shows blind beliefs, opinions and unreasonable adherence to a party, idea, faction or a person. Through this task, we aim to develop an automated system that can be used to detect hyperpartisan news and serve as a prescreening technique for fake news detection. The proposed system jointly uses a rich set of handcrafted textual and semantic features. Our system achieved 2nd rank on the primary metric (82.0% accuracy) and 1st rank on the secondary metric (82.1% F1-score), among all participating teams. Comparison with the best performing system on the leaderboard shows that our system is behind by only 0.2% absolute difference in accuracy.

We describe our system for SemEval-2019, Task 8 on “Fact-Checking in Community Question Answering Forums (cQA)”. cQA forums are very prevalent nowadays, as they provide an effective means for communities to share knowledge. Unfortunately, this shared information is not always factual and fact-verified. In this task, we aim to identify factual questions posted on cQA and verify the veracity of answers to these questions. Our approach relies on data augmentation and aggregates cues from several dimensions such as semantics, linguistics, syntax, writing style and evidence obtained from trusted external sources. In subtask A, our submission is ranked 3rd, with an accuracy of 83.14%. Our current best solution stands 1st on the leaderboard with 88% accuracy. In subtask B, our present solution is ranked 2nd, with 58.33% MAP score.