Vio-Lens: A Novel Dataset of Annotated Social Network Posts Leading to Different Forms of Communal Violence and its Evaluation (2023)

Abstract: This paper presents a computational approach for creating a dataset on communal violence in the context of Bangladesh and West Bengal of India and benchmark evaluation. In recent years, social media has been used as a weapon by factions of different religions and backgrounds to incite hatred, resulting in physical communal violence and causing death and destruction. To prevent such abusive use of online platforms, we propose a framework for classifying online posts using an adaptive question-based approach. We collected more than 168,000 YouTube comments from a set of manually selected videos known for inciting violence in Bangladesh and West Bengal. Using both unsupervised and later semi-supervised topic modeling methods on those unstructured data, we discovered the major word clusters to interpret the related topics of peace and violence. Topic words were later used to select 20,142 posts related to peace and violence of which we annotated a total of 6,046 posts. Finally, we applied different modeling techniques based on linguistic features, and sentence transformers to benchmark the labeled dataset with the best-performing model reaching ~71% macro F1 score.

## introduction with the rise of social media users, different kinds of toxic behavior have been climbing sharply, including hate speech @xcite , online abuse @xcite , and even for terrorist purposes. previous analyses focusing on the in-group and out-group community relationships and conflicts in southeast asia highlighted the role of perceived relative deprivation, economic inequalities, and competitions as the precursor for such communal violence @xcite which is now taking place on social media in a larger scale. in recent years, social media has become a vehicle for inciting violence against minority and underrepresented communities, especially, based on ethnicity, religion, and even nationality around the world, not to mention increasingly in southeast asia. even though there exist different approaches to detect whether an online post has negative sentiment @xcite , or expresses hatred @xcite , and in some cases, the veracity of content @xcite , there is a lack of computational approach to identify violence inciting posts for instigating in-group factions to perform harmful activities on out-group communities by targeting them on social media. most importantly, there is a scarcity of a wellannotated dataset representing different degrees of online violence. violence is rather a much-studied topic in social sciences, especially in peace studies foot_0 @xcite . the term violence can be characterized by a broad spectrum -from a minimalist approach of an intentional act of excessive or detrimental force to an infringement of rights @xcite . preeminent author galtung in his seminal work argued that violence inhibits individuals from realizing their full physical and mental potential, resulting in a gap between what could have been achieved and what actually transpires @xcite . recent studies show that indirect or structural violence, e.g. racism, sexism, heterosexism, xenophobia, and even elitism, can be observed more frequently on social media @xcite . this kind of violence includes the use of political or economic power to commit violent acts or constrain/restrict an individual or a specific group of people. even though those non-physical acts on social media seem unharmful, these activities related to structural violence more often than not translated to physical conflict in southeast asian societies @xcite . therefore, we focus on preparing a dataset on violence incitement by collecting online posts that perpetrated real-life violence across ethnic or communal space, including its detection in bangla. to the best of our knowledge, no existing research has developed a dataset for detecting bangla text that incites violence, based on events leading to significant fatalities and extensive property damage. this paper contributes the following: ## background and motivation drawing from galtung's foundation research work on peace and violence @xcite , violence can be understood as any barrier that hinders individuals from reaching their maximum personal and cognitive development, creating a gap between their possible potential and their lived experiences. numerous instances from our everyday lives can help to elucidate this concept. one particularly poignant incident from bangladesh is the 2021 cumilla durga puja violence that started with a facebook post @xcite . with a staggering 38,005 instances recorded, this event exemplifies how external forces, especially those fueled bangla comment peaceful posts এই রকম েপািসিটভ আেলাচনা সিতয্ই পৰ্েয়াজন। সবাই এেক অপেরর সােথ সহেযািগতা এবং সহেযািগতা িদেয় এিগেয় েযেত পাের। (positive discussions like this are truly needed. everyone can move forward with cooperation and collaboration.) express support for peaceful discussion. যেতাটু কু আমরা সিহষ্ণ ু তা পৰ্দশর্ ন কির, ততটু কু আমরা সবাইেক একেতৰ্ আনেত পাির। সিতয্ই অসাধারণ েপৰ্রণা! (the more tolerance we show, the more we can bring everyone together. truly inspiring!) express solidarity for empathy. যখন আমরা কথা বিল, েসখােন আমরা সবেচেয় বড় পৰ্ভাব ৈতির কির। ধষর্ ণ ছাড়াই সহমিত অনু সন্ধান। (when we communicate, we make the biggest impact. seeking consensus without aggression.) supporting the need of dialogues. by socio-political conflicts and religious tensions, can inhibit the growth and well-being of numerous individuals. such events not only disrupt the immediate safety and security of the people involved but also alter the course of their lives, casting a long shadow on their future prospects. in the subsequent sections, we will explore the ideas of both peace and violence, understanding their definitions, manifestations, and significance in our broader comprehension of societal life. ## baseline creation to establish a violence detection benchmark we explore three different types of modeling techniques in comparison to the baseline method. we discuss the evaluation methods below: we employed three different sentence transformer models, such as multilingual bert (mbert) foot_2 @xcite , distill-bert @xcite foot_3 and xlm-roberta @xcite foot_4 , and monolingual banglabert @xcite 6 . we used hugging face transformers @xcite to finetune the models on our dataset. ## experiments and results we split our dataset into the train set (2700 samples or 45%), the dev set (1330 samples or 22 %), and the test set (2016 samples or 33%) so that nearly 2/3rd of the data is provided for both train set and dev set and the rest 1/3rd of the data is provided for the test set to ensure a good number of data is available for test set prediction. we applied hugging face transformers @xcite , skilit-learn tool @xcite , and the py-torch framework @xcite to carry out our studies. the configurations for the models are discussed in the suppl. we present the test set results of our experiments in error analysis: for the direct violence category, out of 201 test instances, 84.08% was predicted correctly, while 4.48% misidentified as passive violence, and 11.44% were misclassified as non-violence. the passive violence test set comprises 719 samples. of those, 62.73% were correctly classified, while 15.16% were befuddled with direct violence, with the rest erroneously categorizing it under non-violence. for the non-violence category, which had 1,096 samples in the test set, an impressive 82.66% were correctly categorized by all the teams. a minor 7.66% samples were incorrectly identified as direct violence, with the remaining misclassified as passive violence. more details can be found in figure 4 . thus, it can be inferred from the confusion matrix that the best performing banglabert although correctly classified direct violence and non-violence most of the time, has trouble predicting passive violence with a significant number of samples overlapped with both direct-violence and non-violence. ## conclusion in this paper, we propose vio-lens, the first-ever dataset and adaptive categorization procedure of communal violence. through our investigation, we find that banglabert @xcite performs better for our case. we find that banglabert performs the best with an f1 score of 71.07. the dataset and annotation is only applied to the bangla language and incidents and source are limited to the region bangladesh and west bengal of india. therefore, a good direction for our future work will be to gather violence-related data from different regions and different languages and create a baseline from that multilingual dataset. we would also like to expand towards a real-time violence detection model.