B an MANI : A Dataset to Identify Manipulated Social Media News in B angla (2023)

Abstract: Initial work has been done to address fake news detection and misrepresentation of news in the Bengali language. However, no work in Bengali yet addresses the identification of specific claims in social media news that falsely manipulate a related news article. At this point, this problem has been tackled in English and a few other languages, but not in the Bengali language. In this paper, we curate a dataset of social media content labeled with information manipulation relative to reference articles, called BanMANI. The dataset collection method we describe works around the limitations of the available NLP tools in Bangla. We expect these techniques will carry over to building similar datasets in other low-resource languages. BanMANI forms the basis both for evaluating the capabilities of existing NLP systems and for training or fine-tuning new models specifically on this task. In our analysis, we find that this task challenges current LLMs both under zero-shot and fine-tuned set- things

## introduction misinformation is an increasingly pressing concern in the current social and political landscape where information frequently spreads through social media platforms with few constraints to reflect the information in reliable sources. this is further exacerbated by the presence of "bots" made by malicious actors that are designed to artificially spread ideas that distort reality @xcite . in order to mitigate this issue, considerable work has been done to identify fake articles @xcite , verifying scientific 1 our dataset is available at https://github.com/ kamruzzaman15/banmani . and encyclopedia claims @xcite , and identifying claims on social media that distort news from trusted sources @xcite . however, most such work is limited to only english. bangla, with the fifth-most l1 speakers worldwide, at 233.7 million foot_0 , only has prior work in detecting fake articles @xcite . more work in this direction is needed for bangla on social media platforms, as demonstrated by the 2012 ramu incident. in the ramu incident, a facebook post from a fake account led to the destruction of a buddhist temple and dozens of houses in bangladesh by an angry mob of almost 25,000 people @xcite . in this vein, we construct a dataset of news-related social media content for identifying news manipulation in social media, banmani. this dataset is the comparable bangla counterpart to the manitweet dataset @xcite in english. figure 1 shows an example of a reference news article alongside both a manipulated and a non-manipulated social media post. this paper's contributions are the following. ## related work this paper is most closely related to factchecking and fake news detection tasks. while much work in this direction has been done in english, @xcite only recently started work in this domain in bangla by releasing a dataset for fake news detection. in english, @xcite released a dataset for identifying news manipulation in tweets. in order to supplement fully-human data, they used a semi-automatic approach of generating tweets using chatgpt and using human annotators to validate and label the results. they found that chatgpt and vicuna failed to solve this new task, even after fine-tuning. in their work, they used fakenewsnet @xcite dataset to seed their reference articles. fact-checking tasks closely resemble our task in that claims must be compared against reference evidence, such as in the fever @xcite and sci-fact @xcite datasets. techniques for this kind of fact-checking work often use a retrieval module that pulls relevant data from the supplied candidate pool. the degree of consistency between a piece of evidence and the input claim is then evaluated using a reasoning component @xcite . while our task compares text against a reference article, models must be able to separate social media news related to the reference article from those that only convey opinions to ensure the successful completion of our task. this is the key difference between these (i.e., fact-checking and fake news detection) and our work. ## task definition our goal is to identify whether a news-related social media item (a post or a comment) is manipulated. if the social media item is manipulated then furthermore to determine what particular information is being manipulated relative to a related reliable reference article. we divide this task into three parts. subtask 1. first, we identify whether a particular social media item is manipulated. this part is a binary classification task and we consider an item as manipulated if there is at least one manipulated excerpt. subtask 2. second, if a social media item is classified as manipulated then we need to identify which particular excerpt is manipulated. the task then is to identify the excerpt of the social media item which is not consistent with the original reference news article. in our dataset, we refer to any manipulated or newly introduced span as an altered excerpt. subtask 3. the third subtask is to identify the part of the original news article which is manipulated in the social media item. in our dataset, we define the information being manipulated as original excerpt. models must produce an empty string or "none" as the output when the altered excerpt is inserted without modifying any original excerpts. ## banmani: dataset creation we confined our test data collection to facebook since this platform is more commonly used by bangla speakers compared to typically studied media platforms for english speakers (e.g., twitter, instagram, etc.). 3 we have created a dataset that contains 800 news-related social media items with 500 associated news articles. our dataset contains 530 manipulated items and 270 non-manipulated items. the breakdown of our dataset is shown in ## exploratory data analysis from fine-tuned. fine-tuning allows the user to get more out of the available models through provided api. as a result, it can achieve higher quality results than traditional prompt design, train on more examples beyond the limit of traditional prompt, and saves token due to shorter prompts. fine-tuning improves on few-shot learning by training on much more examples that can fit in a prompt. which lets you achieve better results in fine-tuned tasks. in general, fine-tuning involves preparing and uploading training data, training the new fine-tuned model with prepared data, and using the fine-tuned model. for our work, we used gpt-3 (brown et al., 2020) ada foot_8 as our base model due to the unavailability of finetuning for the latest models. also, ada is capable of handling simple tasks and is the fastest model in the gpt-3 series. we used a promptcompletion format for our training data and later fine-tuned our model with this data, resulting in competitive outputs. ## results & analysis the result of the zero-shot chatgpt and our fine-tuned model is presented in ## limitations & future work due to our budget limitation, we were not able to collect a large set of human-written social media items. this means that there exists a gap between the quality of the training and test data; the training set was automatically created, unlike the test data. in the future, we will collect more human-written items from social media to create an entirely human-written training dataset. our prompts are also purposefully simple, as this was the first step in creating such a dataset. we expect to get qualitative gains in the automatically generated data with more careful prompt engineering. finally, our experiments were limited to only a single popular llm for each setting. expanding the experiments to cover other llms, especially open-source llms would lead to more robust experimental results and better replicability. we also leave the few-shot method as our future work. ## conclusion in this paper, we presented the banmani dataset, the semi-automatically constructed dataset of news manipulation in social media. this dataset extends huang et al.'s 2023 manitweet dataset to bangla. our semiautomatic collection process generates social media posts from seed articles using a multilingual llm and a bangla ner system. these results are filtered using human annotators for efficient use of annotator time. we find that both zero-shot and fine-tuned llms struggle on this dataset, pointing to important direc-tions of future work. surprisingly, we find that llms perform similarly effectively on this dataset when compared to the english variant. we hope that this new resource can help with combating information manipulation in bangla-speaking social media communities. furthermore, we believe that the technique laid out here can act as a basis for similar work in other under-served languages in nlp.