“A Little Birdie Told Me ... ” - Inductive Biases for Rumour Stance Detection on Social Media (2020)

Abstract: The rise in the usage of social media has placed it in a central position for news dissemination and consumption. This greatly increases the potential for proliferation of rumours and misinformation. In an effort to mitigate the spread of rumours, we tackle the related task of identifying the stance (Support, Deny, Query, Comment) of a social media post. Unlike previous works, we impose inductive biases that capture platform specific user behavior. These biases, coupled with social media fine-tuning of BERT allow for better language understanding, thus yielding an F1 score of 58.7 on the SemEval 2019 task on rumour stance detection.

## introduction social media has seen an exponential growth, replacing traditional news sources as the primary news source. the apparent value of interesting truth-like news and the ease of access to such news jointly make social media a hotbed for rumours, misinformation and fake news. in the absence of an authority to verify or debunk a rumour, social media users often share their own thoughts on its veracity, creating a collaborative inter-subjective sense-making to determine the veracity of the rumour. hence, an important step in achieving the objective of veracity detection is tracking how other users opine on the accuracy of the rumourous story @xcite . the content on these social media platforms vary in their topics, style, sentiments, and structure @xcite . reddit, for example, is used for gathering a comprehensive view of opinions from users in a short period of time. on the other hand, an event on twitter is alive for a longer duration and is used for following the development and evolution of an event @xcite . ## task definition to judge the veracity of a social media post, it is useful to analyze the surrounding discourse (comments/replies) by other users. the discourse is initiated by a source post and followed by treestructured threads. each post in a thread is made in response to a parent post that immediately precedes it. this problem was modeled as a semeval shared task -rumoureval @xcite , consisting of two subtasks. a. stance classification -given a source post introducing a rumour and the ensuing conversation thread, classify the source and each post in the thread into one of 4 categories. ## related work this section highlights prior work on the ru-moureval dataset. ## system description our system (figure 1 ) utilizes the content from the source and parent tweets as additional context following previous work @xcite which noted that the above two tweets mostly contain sufficient information to classify a target tweet correctly. in this work, we leverage various inductive biases and propose late fusion in §4.1, social media fine-tuning to better leverage bert in §4.2, discrimination between social media platforms in §4.3, domain-specific features over generic textual features in §4.4, and transition priors to better capture conversation dynamics in §4.5. ## experimental setup we use the huggingface transformers library foot_1 to fine-tune bert base on the sequence classification task. we use the adam optimizer (kingma and ba, ## results and error analysis our approach achieves an f 1 of 58.7, outperforming non-ensemble approaches by 2 f 1 as shown in ## unsolvable examples the rumoureval dataset contains examples with noisy annotations ( another class of unsolvable examples stemmed from deleted tweets. if a particular tweet was deleted, the dataset attaches its children to their grandparent tweet. this presents issues as the children express opinions towards a deleted tweet. a potential solution would be to remove tweets where the '@' mention is towards an unseen author but we would risk further reducing the small number of training examples in our dataset. ## conclusion and future work in this work, we showcased the efficacy of inductive biases to the task of stance classification and achieved a score of 58.7 f 1 , surpassing existing approaches. we hope to utilize this model in other downstream tasks like veracity detection (task b) and expand our inductive biases to other social media tasks such as fact verification and conversation derailment detection.