WICO Text: A Labeled Dataset of Conspiracy Theory and 5G-Corona Misinformation Tweets

The COVID-19 pandemic has been accompanied by a flood of misinformation on social media, which has been labeled an "infodemic". While a large part of such fake news is ultimately inconsequential, some of it has the potential to real-world harm, but due to the massive amount of social media contents, it is impossible to find this misinformation manually. Thus, conventional fact-checking can typically only counteract misinformation narratives after they have gained significant traction. Only automated systems can provide warnings in advance. However, the automatic detection of misinformation narratives is very challenging since the texts that spread misinformation may be short messages on Twitter. They may also transmit misinformation by implication rather than by stating counterfactual information outright, and satirical messages complicate the issue further. Thus, there is a need for highly sophisticated detection systems. In order to support their development, we created substantial ground truth data by human annotation. In this paper, we present a dataset that deals with a specific piece of misinformation: the idea that the COVID-19 pandemic is causally connected to the 5G wireless network. We selected more than 10,000 tweets that deal with COVID-19 and 5G and labeled them manually, distinguishing between tweets that propagate the specific 5G misinformation, those that spread other conspiracy theories, and tweets that do neither. We provide the human-annotated dataset along with an additional large-scale automatically (by using the human-annotated dataset as the training set) labelled dataset consist of more than 100,000 tweets. © 2021 ACM.

WICO Graph: A Labeled Dataset of Twitter Subgraphs based on Conspiracy Theory and 5G-Corona Misinformation Tweets

In the wake of the COVID-19 pandemic, a surge of misinformation has flooded social media and other internet channels, and some of it has the potential to cause real-world harm. To counteract this misinformation, reliably identifying it is a principal problem to be solved. However, the identification of misinformation poses a formidable challenge for language processing systems since the texts containing misinformation are short, work with insinuation rather than explicitly stating a false claim, or resemble other postings that deal with the same topic ironically. Accordingly, for the development of better detection systems, it is not only essential to use hand-labeled ground truth data and extend the analysis with methods beyond Natural Language Processing to consider the characteristics of the participant's relationships and the diffusion of misinformation. This paper presents a novel dataset that deals with a specific piece of misinformation: the idea that the 5G wireless network is causally connected to the COVID-19 pandemic. We have extracted the subgraphs of 3,000 manually classified Tweets from Twitter's follower network and distinguished them into three categories. First, subgraphs of Tweets that propagate the specific 5G misinformation, those that spread other conspiracy theories, and Tweets that do neither. We created the WICO (Wireless Networks and Coronavirus Conspiracy) dataset to support experts in machine learning experts, graph processing, and related fields in studying the spread of misinformation. Furthermore, we provide a series of baseline experiments using both Graph Neural Networks and other established classifiers that use simple graph metrics as features. The dataset is available at https://datasets.simula.no/wico-graph..

Evaluating standard classifiers for detecting COVID-19 related misinformation

This paper summarises the results created through participation in the task FakeNews: Corona Virus and 5G Conspiracy of the MediaEval Multimedia Evaluation Challenge 2020. The task consists of two parts intending to detect tweets and retweet cascades that emerged during the COVID-19 pandemic and causally connect the radiation of 5G networks with the virus. We applied several well-established neural networks and machine learning techniques for the first subtasks, namely, textual information classification. For the second task, the retweet cascades analysis, we rely on classifiers that work on established graph features, such as the clustering coefficient or graph diameter. Our results show a MCC-score of 0.148 or 0.162 for the NLP task and 0.02 for the structure task. © 2020 Copyright 2020 for this paper by its authors. All Rights Reserved.