Team C entre B ack at BLP -2023 Task 1: Analyzing performance of different machine-learning based methods for detecting violence-inciting texts in B angla (2023)

Abstract: Like all other things in the world, rapid growth of social media comes with its own merits and demerits. While it is providing a platform for the world to easily communicate with each other, on the other hand the room it has opened for hate speech has led to a significant impact on the well-being of the users. These types of texts have the potential to result in violence as people with similar sentiments may be inspired to commit violent acts after coming across such comments. Hence, the need for a system to detect and filter such texts is increasing drastically with time. This paper summarizes our experimental results and findings for the shared task on The First Bangla Language Processing Workshop at EMNLP 2023 - Singapore. We participated in the shared task 1 : Violence Inciting Text Detection (VITD). The objective was to build a system that classifies the given comments as either non-violence, passive violence or direct violence. We tried out different techniques, such as fine-tuning language models, few-shot learning with SBERT and a 2 stage training where we performed binary violence/non-violence classification first, then did a fine-grained classification of direct/passive violence. We found that the best macro-F1 score of 69.39 was yielded by fine-tuning the BanglaBERT language model and we attained a position of 21 among 27 teams in the final leaderboard. After the competition ended, we found that with some preprocessing of the dataset, we can get the score up to 71.68.

## introduction with the rise of the internet, it has become easy to post and comment on multiple social media platforms. ease of access means that people have the power to influence others to commit violent acts. early detection and removal of these type of content is necessary to avoid regrettable events such as killing, rape or mass murder. to this end, the violence inciting text detection (vitd) shared task @xcite ) was introduced at the bangla language processing workshop at emnlp 2023. to the best of our knowledge, the shared task and its accompanying dataset @xcite are the first of its kind. while previous work explored similar tasks such as hate speech detection in bangla (ishmam and sharmin, 2019), @xcite , this task is the first of its kind to call for systems that can classify a given text as likely to incite violence or not. it has a further fine-grained classification label for violence-inciting texts -namely passive and direct. in this paper, we discuss our submitted systems to the shared task. we present our analysis related to the dataset and also the models that were submitted. 1 . the paper is organized as follows : first, we analyze the dataset, then we describe the experiments performed both before and after the competition ended. finally, we analyze the systems submitted and discuss their shortcomings, along with possible directions for future work. the accompanying code for our experiments and analysis is publicly made available. foot_1 ## dataset overview the dataset was created compiling youtube comments in bangla associated with the top 9 violent incidents that have occurred in the bengal region (bangladesh and west bengal) within the past 10 years. comments which stated facts or referred to any kind of social discussion were classified under the non-violence category. for comments which contained opinions in a derogatory language and statements which attempted to justify violence were classified under the passive violence category. fi- ## dataset analysis the comments in the dataset have a maximum comment length of up to 600 words. it covers a wide range by considering comments from over the past 10 years. however, it lacks equality in the amount of data it has for the three different categories. the ratio of non-violence to violence does seem like a roughly equal distribution from a high level. in spite of that, if we dive deep we see that the ratio of passive violence to direct violence is roughly 2:1. the complete and extensive view of the dataset showing the actual percentages of the comments under each category is given in figure 1 . although we do have a total of around 1350 comments for both non-violence and violence categories, if we only consider the violence categories then we do not have the direct and passive violence categories equally distributed. on one hand for direct violence category we have a deficit of around 300 and on the other hand we have a surplus of 300 for the passive violence category. hence, we can conclude that we have a significantly low amount of data for the direct violence category and overall have an imbalance of data in the dataset. besides, confusion arises for multiple comments where according to the description it should have been identified as one category but it was denoted as another category. examples are shown in furthermore, throughout the dataset instead of portraying religion in a non-discriminative way a biasness was observed towards islam or hinduism. religion was displayed in more of a negative tone which was further strengthened by the lower number of neutral religious comments across the whole dataset. this in turn can lead to a potential misuse and give rise to some ethical concerns. to add to it, the dataset consists of only bengal regions (bangladesh and west bengal). also, since the only source for the dataset was youtube comments, a wide range of other diverse origins like educational sites such as encyclopedias, blogs, e-books and factual sites like news channel websites were not taken into account. thus, considering a worldwide perspective a major portion of variety was missing. to summarize, there was an absence of impartiality in terms of religion and regarding the scope a more narrowed down sense was noticed throughout the whole dataset. moreover, lots of repetition and redundancy was seen on all parts of the dataset. not only are there recurrences of comments but also loads of spelling mistakes at frequent intervals. example comments which show repetition in comments are shown in also, comments should normally have a variety in the speaking tone. however, no such steps were 2 169 additionally, similar token length values were used for all the categories. however, the value taken was comparatively a very small value. the mean value was around 19.6 and the standard deviation value was around 16.6. hence, the deviation from the mean was very high. in ideal scenarios we would expect the standard deviation to be as low as possible. standard deviation and mean token length values are shown in finally, it is worth noting that the emojis have a significance while denoting the category labels. for example, for the comment shown in ## system overview for all the experiments, we have used either nvidia tesla v100 gpu or t4 gpu provided by google colaboratory, depending on the availability. during our submission for the competition we did not consider any preprocessing for the dataset and focused fully on the methodology of the model. after the competition ended, we performed some preprocessing to remove punctuations completely from the dataset. it is important to note that, in the competition, there were two phases -in the first round, we were provided a test set with the ground truth labels, while in the second phase, we had a hidden test set, whose labels were provided after the competition ended. thus, we have reported our results (in in this competition, the evaluation metric was the macro-f1, which takes the arithmetic mean of the per-class f1 scores. the f1-score is calculated for each class in the following way foot_2 -2 * precision * recall precision+recall , where precision tells us what fraction of the positive predictions are correct, and recall tells us what fraction of the positive labels have been correctly identified. here, positive label means that the comment belongs to the class, for which f1-score is being calculated. ## error analysis after the competition ended, we performed further analysis to determine the reason for the comparatively low macro-f1 scores. in the test set, we noticed that among the misclassifications done by our best model, a portion of those comments had a lot of repetitive punctuations as shown in ## future works although we tried out different methods but our system did not take into account a number of things. firstly, the spelling mistakes and missing spaces between two independent words in both training and inference stages. secondly, the significance of emojis was also not taken into consideration. furthermore, additional knowledge bases for fine-tuning could have also been used to see if it solves the issue with the limited dataset. lastly, the repetition of similar comments throughout the whole dataset was also not taken into account. the points mentioned above can be considered for future work for improving violence inciting text detection. in addition, the performance of large language models can also be investigated in this task, as they have been recently shown to perform well on different nlp tasks. @xcite ## conclusion in this paper, we have presented our experiments and findings for the blp shared task 1 : violence inciting text detection. initially, we provide a detailed analysis of the dataset, showing statistics and discussing problems with the dataset. we have found that banglabert fine-tuned for 20 epochs gives us the best macro-f1 score of 69.39. after the competition ended, we analyzed the possible reasons for misclassifications. to further explore and overcome some of those causes, we conducted different experiments that led to a further improvement, taking the macro-f1 score to 71.68. finally, we discussed the shortcomings of our system and the various possible directions for future work that can improve the detection of violenceinciting texts.