Social media users are more likely to be exposed to similar views and tend to avoid contrasting views, especially when they are part of a community of social media users. In this study, we investigate the presence of user communities and leverage them as a tool to detect misinformation on social media, specifically on X (formerly known as Twitter). We propose a misinformation detection framework, namely Similarity-based Misinformation Detection (SiMiD) that employs microblogs and utilizes user-follower interactions within a social network. Our approach extracts important textual features of social media posts using a transformer-based language model. We use contrastive learning and pseudo-labeling to fine-tune the language model. Then, we measure the similarity for each social media post based on its relevance to each user in the communities. Finally, we train a machine learning model to identify the truthfulness of social media posts using these similarity scores. We evaluate our approach on three social media datasets, compare our method with twelve state-of-the-art approaches, and answer five research questions. The experimental results, supported by statistical tests, show that contrastive learning and user communities can enhance the detection of misinformation on social media. Our model can identify misinformation content by achieving a consistently high weighted F1 score of over 90% across all datasets, even employing only a small number of users in communities. We make our implementations publicly available and provide all details that are necessary for the reproducibility of experiments. 1

The rapid dissemination of misinformation through online social networks poses a pressing issue with harmful consequences jeopardizing human health, public safety, democracy, and the economy; therefore, urgent action is required to address this problem. In this study, we construct a new human-annotated dataset, called MiDe22, having 5,284 English and 5,064 Turkish tweets with their misinformation labels for several recent events between 2020 and 2022, including the Russia-Ukraine war, COVID-19 pandemic, and Refugees. The dataset includes user engagements with the tweets in terms of likes, replies, retweets, and quotes. We also provide a detailed data analysis with descriptive statistics and the experimental results of a benchmark evaluation for misinformation detection.

Tokenization is an important text preprocessing step to prepare input tokens for deep language models. WordPiece and BPE are de facto methods employed by important models, such as BERT and GPT. However, the impact of tokenization can be different for morphologically rich languages, such as Turkic languages, in which many words can be generated by adding prefixes and suffixes. We compare five tokenizers at different granularity levels, that is, their outputs vary from the smallest pieces of characters to the surface form of words, including a Morphological-level tokenizer. We train these tokenizers and pretrain medium-sized language models using the RoBERTa pretraining procedure on the Turkish split of the OSCAR corpus. We then fine-tune our models on six downstream tasks. Our experiments, supported by statistical tests, reveal that the morphological-level tokenizer delivers a challenging performance with de facto tokenizers. Furthermore, we find that increasing the vocabulary size improves the performance of Morphological- and Word-level tokenizers more than that of de facto tokenizers. The ratio of the number of vocabulary parameters to the total number of model parameters can be empirically chosen as 20% for de facto tokenizers and 40% for other tokenizers to obtain a reasonable trade-off between model size and performance.