FakeNews: Corona Virus and 5G Conspiracy Task at MediaEval 2020

Abstract

The FakeNews: Corona Virus and 5G Conspiracy task, running for the first time as part of MediaEval 2020, focuses on the classification of tweet texts and retweet cascades for the detection of fast-spreading misinformation, and therefore provides a lowthreshold introduction to natural language processing and graph analysis. This paper describes the task, including use case and motivation, challenges, the dataset with ground truth, the required participant runs, and the evaluation metrics.

INTRODUCTION

Digital wildfires, i.e., fast-spreading inaccurate, counterfactual, or intentionally misleading and information that can quickly permeate public consciousness and have severe real-world implications, are among the top global risks in the 21st century [ 10 ]. While misinformation is widespread on the internet, only a very small portion of it leads to harmful harmful acts in the real world. In2020. the COVID-19 pandemic has severely afected people worldwide and consequently dominated world news for months. Thus, it is no surprise that it has also been the topic of a massive amount of misinformation, which was most likely amplified by the fact that many details about the virus were unknown at the start of the pandemic. We are particularly interested in detecting content associated with a Digital Wildfire that relates COVID-19 to 5G wireless technology and led to arson and attacks on telecommunications workers. Despite the emphasis on COVID-19 and 5G, we further diferentiate between content that does not contain misinformation and content attributed to other misinformation. Our task ofers two subtasks: The first subtask includes text-based tweets classification, while the second targets the classification of retweet cascades [ 11 ].

In contrast to text-only classification challenges, e.g., [ 1, 7, 13 ], our dataset also contains retweet cascades, allowing us to consider difusion as a characteristic shown to be valuable for the spread of misinformation [ 20 ]. The final goal is the inclusion of various field experts aiming for eficient multi-modal approaches. Furthermore, we ask for evaluation of diferent approaches utilizing both as little and as much training data as possible and evaluating the approaches with respect to real-world imbalanced datasets [ 2 ].

There are already many methods for automatic news analysis and fake content detection in the social media and news analysis ifeld, e.g. [ 3, 5, 12, 16 ] that cover a wide range of approaches, including knowledge graphs, difusion models, and natural language processing. These methods typically rely on labeled data. Consequently, several such datasets have been published in recent years [ 4, 6, 8, 14, 15, 17, 19, 21 ]. However, to our best knowledge, there is no existing dataset that emphasizes Digital Wildfires and takes retweet cascades into account.

The task is intended to be of interest to researchers in the areas of online news, social media, multimedia analysis, multimedia information retrieval, natural language processing, and meaning understanding and situational awareness. 2

DATASET DETAILS

Our dataset’s creation can roughly be divided into four steps. First, we used Twitters’search API between January 17, 2020 and May 15, 2020 to collect a large number of statuses (i.e. tweets, retweets, quotes, and replies) including key-words related to the COVID-19 pandemic. Moreover, we filtered for those that mention 5G in any conceivable spelling such as 5G, 5g, or #5g. Second, we restored as much of the Twitter threads as possible using our custom framework [ 18 ]. The result is a graph of tweets, retweets, and quotes that does not only consist of statuses containing the obvious combination of keywords but provides more subtle content like "All this to declare martial law huh? Lol or do you wanna put fear in us so we can run and get them vaccines to make us suseptible for these damn 5g tower radiations? Lol either way, the government is to NOT be trusted and are up to something.". Some threads containing these statuses have their origin long before the COVID-19 pandemic. Nevertheless, we decided to include this data, too, since it contains context that led to our Digital Wildfires’ emergence. In the third step, based on the number of statuses obtained in steps one and two, we started the manual labeling. Therefore, we randomly selected a subset of 10 tweets with their corresponding retweets. The annotation process has been performed by a team of researchers, postdocs, Ph.Ds, and master students. Each team member received an part of the subsets and these data were then annotated manually. Most of the easy-to-annotate statuses were assessed and classified by one annotator, but when assigning a class was not obvious, the tweet was discussed with the entire group until consensus was reached. While the text dataset was prepared via manual labelling, extracting the retweet cascades requires an additional step. A cascades root is always a labeled tweet while all other nodes correspond to retweets. We again made use of Twitter’s API to fetch these retweets and the underlying social network that connects users via follower relationships. Unfortunately, Twitter limits the number of available retweets which narrows the cascade size to one hundred. Since each tweet and retweet contains a timestamp, one can track the temporal difusion. However, Twitter does not provide the true retweet path, thus leaving it to the challenge participants to reconstruct it. We use three classes to label tweets and retweet cascades: The 5GCorona Conspiracy class corresponds to all tweets that claim or insinuate some deep or obvious connection between COVID-19 and 5G, such as the idea that 5G weakens the immune system and thus caused the current Corona-virus pandemic, or that there is no pandemic and the COVID-19 victims were actually harmed by radiation emitted by 5G network towers. The crucial requirement is the claimed existence of some causal link. The Other Conspiracy class corresponds to all tweets that spread conspiracy theories other than the ones discussed above. This includes ideas about an intentional release of the virus, forced or harmful vaccinations, or the virus being a hoax. The Non-Conspiracy class corresponds to all tweets not belonging to the previous two classes and includes those discussing COVID-19 pandemic itself, claiming that 5G is not proven to be absolutely safe or even can be harmful without linking it to COVID-19, as well as claiming that authorities are pushing for the installation of 5G while the Publicis distracted by COVID-19. In addition, tweets pointing out the existence of conspiracy theories or mocking them fall into this class since they do not spread the conspiracy theories by inciting people to believe in them. 2.1

Dataset Contents

The development and test datasets consist of 6, 458 tweets and 2, 327 retweet graphs, and 3, 230 tweets and 1, 165 retweet graphs respectively, stored in two folders each: tweets and graphs. Both datasets are heavily unbalanced in terms of the number of samples per class, reflecting the distribution of tweet topics and people’s opinions. To comply with the Twitter data publication policy, we provide only tweet IDs, but not the tweet text itself. An additional tweet content download script is provided to obtain the tweets from their ids via the corresponding Twitter API using a user-supplied API access keys. Retweet cascades are stored individually in a separate folder with three files. The edges.txt file contains a directed edge list source-node-ID to target-node-ID. The plot.png file contains a plot of the cascade. The nodes.csv contains an assignment from the node ID to the following properties: id - an anonymized node ID which remains the same for all graphs in the dataset of all categories; time - the time diference in seconds from each retweet to the original tweet. The original tweet always has a diference of 0 seconds to itself; friends - the next greater power of two of the follower count from the user profile of the respective user; followers - the next greater power of two of the friend count from the user profile of the respective user. 3

EVALUATION METRICS AND SUBTASKS

The oficially reported metric used for evaluating the multi-class classification performance is the multi-class generalization of the Matthews correlation coeficient (MCC) [ 9 ]. In case of equal metric values, we use the timestamp of the oficial run submission to rank the teams. For the evaluation, the participants must submit one run for both subtasks defined below. Additionally, they optionally can submit four more runs for any of the described subtasks, i.e., participants can submit up to ten runs in total.

Text-Based Misinformation Detection Subtask: In this subtask, the participants are asked to perform classification of the tweets based on the tweet text contents and other tweet-relevant multimedia and meta-information can be obtained from Twitter or the Internet. The subtask requires one mandatory and four optional runs to be submitted. The required run implements a pure NLP classification of tweets based only on tweet text content without using any additional sources of data. Optional runs gradually extend the amount and types of allowed additional information implementing classification based on tweet text analysis in combination with visual information (images and/or videos) extracted from the original tweet and classification using any automatically scraped data from any external sources.

Structure-Based Misinformation Detection Subtask: In this subtask, the participants are asked to perform a classification of tweet graphs based on the tweet retweet graph, and additional retweet-tree-related information was obtained from Twitter. The subtask requires one mandatory and four optional runs to be submitted. The required run implements a pure tweet classification based only on the retweet graph structure only, without using any additional data. Optional runs gradually extend the amount and types of allowed additional information implementing classification based on a full set of retweet graph description, retweeting nodes’ properties, and using any automatically scraped data from any external sources.

Thus, the participants are allowed to use only information that can be extracted from the provided tweets (including metadata) and retweet cascades for generating the first and second run for both subtasks. In contrast, for other runs everything is allowed, both from the data collection method perspective and the sources of information used. However, manual annotation of tweets or any externally scraped data is not allowed in any run. 4

DISCUSSION AND OUTLOOK

The task itself can be seen as very atypical and challenging due to a fairly limited amount of information available to support the tweet classification process. This reflects the real-world conditions in which online social media analysis systems are deployed. Thus, this task is a practical attempt to make a step towards building a usable multi-modal social network analysis system that is able to combine isolated data source properties with inter-source relations. Due to the importance of the use case, we hope to motivate researchers from diferent research fields to present their approaches, thereby performing research that can help society to fight against malicious manipulations of social networks and threats to society in general. We hope that the FakeNews task can help to raise awareness of the topic, but also provide an interesting and meaningful use case to researchers interested in this application.

ACKNOWLEDGMENTS

This work was funded by the Norwegian SAMRISK-2 project ”UMOD” (#272019). It has benefited from the Experimental Infrastructure for Exploration of Exascale Computing (eX3), which is financially supported by the Research Council of Norway under contract 270053.

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