# A Survey on the Use of Graph Convolutional Networks for Combating Fake News
## 4 Datasets  
The works presented in this survey either employ some standalone (i.e., without link or any other dispersion information) text corpora and classify them in a binary classification task or employ Twitter for creating custom data collections. In the latter case, they take advantage of the fact that Twitter provides both the textual content of tweets, as well as the profile information of Twitter users and information about the retweets, which can be used to model the news dispersion. In the case of news classification tasks, public real-world social media datasets have been employed in the literature such as:  
* PHEME [43]: A Twitter dataset containing 5802 annotated tweets, of which 34% are rumors. A collection script is also made available by the authors ([[https://github.com/azubiaga/pheme-twitterconversation-collection](https://github.com/azubiaga/pheme-twitterconversation-collection)]([https://github.com/azubiaga/pheme-twitterconversation-collection](https://github.com/azubiaga/pheme-twitterconversation-collection)), accessed on 17 February 2022). * Weibo [44]: 3.8 million weibo.com posts from 2.7 million users of Weibo discussing 4664 events, of which 50% are rumors. * LIAR [45]: 12,836 labeled short statements with six fine-grained truthfulness labels, from totally fake to completely true. * MediaEval Twitter [46]: A dataset with around 17,000 unique tweets spanning over different events, comprising 9000 fake news tweets, 6000 real news tweets in the development set and 2000 tweets in the test set. The tweets comprise text, images and video. * Twitter15 and Twitter16 [47]: Comprising 331,612 posts from 276,663 users on 1490 events and 204,820 posts from 173,487 users on 818 events, respectively, classified in four rumor classes. Figure 5: An overview of the rumor detection process using GCN. * Fakeddit [48]: This multimodal (text and images) dataset comprises over 1 million samples from multiple subreddits from Reddit, split into 2, 3 or 6 classes, along with comment data and metadata. For the detection of bot or spam accounts that create rumors the list of datasets employed comprises:
* TwiBot-20 [49]: A publicly available Twitter bot detection dataset that provides the follow relationship between users for 230,000 users. * RTbust [50]: A Twitter dataset comprising 9,989,819 retweets related to 1,691,865 distinct original Italian tweets shared by 1,446,250 distinct users. The dataset contains social bots and user-operated accounts and covers the need of OSN administrators that look for automated behaviors in retweeting in order to decide about banning accounts from social platforms. * Bootometer-feedback [51]: A mix of training data from eight previously published works containing 57,155 bot and 30,853 accounts. * Gilani ([[https://goo.gl/SigsQB](https://goo.gl/SigsQB)]([https://goo.gl/SigsQB](https://goo.gl/SigsQB)), accessed on 17 February 2022) [52]: Approximately 65 million tweets (2-2.5 million per day) from 2.9 million unique accounts. * SFAKE [53]: A dataset comprising stock microblogs collected from Twitter. A total of 6689 stocks were used to fetch the content from Twitter and resulted in 9 M tweets (22% are retweets) posted by 2.5 M distinct users. The tweets mention 30,032 companies for which financial information was collected from Google Finance. * Midterm [54]: An amalgam of 14 other datasets that consists of 94,124 bot accounts (lured by honeypot accounts) and 43,396 verified human accounts. * Twitter Social [55]: A dataset, which has been collected using 60 social honeypot accounts on Twitter that tweet normal posts, links or popular n-grams in order to tease their followers to retweet. The social honeypots tempted 36,043 Twitter users, 5773 (24%) of which followed more than one honeypot and 23,869 followed only one honeypot. * Twitter 1 KS-10 KN [56]: A dataset comprising 485,721 Twitter accounts with 14,401,157 tweets and 5,805,351 URLs. From these accounts, 10,004 are considered malicious affected accounts that post malicious URLs.