Toward stance parameter algorithm with aggregate comments for fake news detection

In the detection of fake news, the stance of comments usually contains evidence supporting false news that can be used to corroborate the detected results of the fake news. However, due to the misleading content of fake news, there is also the possibility of fake comments. By analysing the position of comments and considering the falseness of comments, comments can be used more effectively to detect fake news. In response to this problem, we proposed Bipolar Argumentation Frameworks of Reset Comments Stance (BAFs-RCS) and Average Parameter Aggregation of Comments (APAC) to use the stance of comments to correct the prediction results of the RoBERTa model. We use the Fakeddit dataset for experiments. Our macro-F1 results on 2-way and 3-way are improved by 0.0029 and 0.0038 compared to the baseline RoBERTa model's macro-F1 results at Fakeddit dataset. The results show that our method can effectively use the stance of comments to correct the results of model prediction errors.