Capturing the Spectrum of Social Media Conflict: A Novel Multi-objective Classification Model

Venue: ACM_org_SIGIR_ICTIR_2024_Conference
Published: 2024-04-24 16:21:38

Abstract: Social media has emerged as a widespread phenomenon, with numerous users engaging in observing, creating, and distributing content. The growing content has led to user conflicts, encompassing bullying, aggression, harassment, and threats. Consequently, recent research has aimed to identify and address these openly hostile forms of social conflict. However, the less overtly hostile yet equally damaging types of conflict, including teasing, criticism, and sarcasm, have been overlooked in current studies.

Our aim is to detect these subtle forms of conflict, while also including openly hostile forms, by developing a novel multi-objective classification model. This innovative approach leverages class based reward functions to improve model performance. Reward functions serve as potent signals capable of mitigating the intricacies of misclassification in multi-class scenarios. By incorporating various rewards within the model architecture, harnessing the power of a decision transformer, we achieved significant improvements in classification performance.  Our experiments on three datasets demonstrate superior recall, precision, f1-score, and accuracy compared to traditional state-of-the-art deep learning classifiers. Furthermore, we analyse class ambiguity and its impact on model performance as well as conducting thematic analysis on model misclassifications. We will share the code and datasets at github.com/anonymous.

Main paper: 
 introduction: 
 Social media has evolved into the primary avenue for interpersonal communication, with extensive research and evidence supporting its widespread usage within society [5, 56, 16] . Social media users disseminate their opinions, views, and thoughts to a broad, global audience. Consequently, increasing negative behaviours and interactions on social media platforms have been observed [2, 63, 8] . Several factors contribute to the escalation of these detrimental behaviours encompassing; anonymity, the absence of tangible physical and social cues, and the absence of accountability for users who propagate harmful content [33] . All of these factors lead to a toxic online environment, with severe consequences to the well-being of individuals and communities 1 .Existing research methodologies aim to discern overtly hostile and socially damaging negative actions, such as hate speech, aggression, and cyberbullying, primarily due to their evident nature [28, 4, 57] . Nevertheless, it has been shown that more subtle negative behaviours, encompassing sarcasm and teasing, wield a significant detrimental influence on user well-being [10, 41, 70, 45] , as their perceived harm varies subjectively. We contend that lighter conflicts, such as criticism, sarcasm, and teasing, form part of a spectrum of negative behaviours. This spectrum includes more extreme behaviours like harassment and threats, which have received more extensive research attention. For instance, all depicted user behaviours in Poletto et al. [57] , are situated at the end of the spectrum. The lesser behaviours, though not directly overlapping, as slightly intersecting with toxic and occasionally offensive behaviour, primarily existing in an adjacent category. Nonetheless, no research focuses on detecting these broader manifestations of conflicts, and hence it becomes imperative to investigate a diverse spectrum of adverse user interaction behaviours on social media, collectively referred to as "conflicts".With the increasing recognition of conflicts as a significant research area and a growing social issue [28] , there has been a rise in the availability of datasets and models focused on the phenomenon [50, 1] . Existing research has centered on binary hate datasets and published papers, with fewer multi-label and multi-class datasets available [23, 27, 52] . While these resources have contributed valuable insights, it is essential to note that current research datasets have limitations and do not encompass a wide range of social media conflicts. Hence, our work utilises a multi-class conflict dataset, developed exploiting hate netnographies [43] , which has not yet been used in multi-class classification tasks.In multi-class classification, a substantial challenge arises due to the inherent interaction between distinct classes, leading to heightened complexities in distinguishing and isolating specific class instances. Particularly within multi-class scenarios, the occurrence of cross-talk between classes introduces a significant hurdle, consequently impacting the effectiveness of classification [44] . This challenge is worsened in social media scenarios due to the ambiguity, noisy and error-prone constitution of social media data [11] . In addition, the conventional strategy of constructing test collections from social media, especially exploiting distant supervision, contributes to this situation's intricacies. In hate classification research, despite other researchers identifying the complexity and ambiguity of defining these behaviours, there has been no discussion on the profound impact of these issues on classification performance.Due to the lack of this type of research, we investigate detecting conflicts effectively and experiment with social media data in prominent consumer brand communities. Brand communities are pages, groups, and timelines controlled by brands which provide the opportunity for consumers to interact with the brand and each other [15] . These communities embrace substantial global user cohorts, thereby rendering them of notable significance for consumer conflict research. Prior work within the marketing domain has underscored the challenges brands encounter in relation to conflicts. These challenges include but are not limited to; serious damage to brand reputation, harm to consumers well-being, withdrawal of users from the brand community, and consumer purchase intentions [14, 72] . Therefore, it is essential to undertake a more comprehensive exploration of these conflicts. In addition, we experiment with two other publicly available datasets to demonstrate the generalisability of our approach to existing tasks within the domain alongside the wider range of conflicts we propose here.As discussed, current approaches fail to capture the spectrum of conflicts, focusing research upon methods which detect only the extreme forms of hate. Therefore, in this paper, we propose a novel approach that involves framing the classification problem as a supervised learning task utilizing class based rewards within the classification model to encourage specific model behaviour. To this end, we leverage the Decision Transformer Model [19] , which has exhibited success in domains involving vision and language modelling [46, 37] . However, its application in the realm of text classification remains largely uncharted. The Decision Transformer model introduces a pioneering strategy for transforming reinforcement learning problems into sequential decision-making problems by utilising the Transformer architecture [67] . Nevertheless, transitioning reinforcement learning models to classification scenarios is more complex. Hence, we devise an innovative approach by exploiting the reward functionality aspect of the Decision Transformer framework, introducing a novel class-based reward computation mechanism. The focal point of this reward function is to optimize class distances with the overarching goal of enhancing classification performance.We make the following contributions in this paper:• We formulated multi-class text classification as a supervised learning problem incorporating novel reward functionality and developed an effective end-to-end classifier, called con-flictDT. • We investigate the role of reward functions to encourage specific classification behaviours. • We evaluated this approach in three datasets using multiple metrics and with statistical validation. One of these is a novel multi-class conflict dataset containing a spectrum of negative social media behaviours. • We analyse classifier performance beyond traditional metrics, conducting a popular social science technique, thematic analysis, to investigate misclassification trends. 
related work: 
 There are a multitude of works investigating hate detection that have developed various machine-learning models and explored different classification algorithms and feature representations. Advancements in deep learning have increased the effectiveness significantly [51] . Deep learning algorithms, including BERT [24] , LSTM [35] , and CNN [55] , have demonstrated impressive capabilities across different text classification tasks. Khan et al. [39] proposed a CNN-based framework called HateClassify for labelling social media content, achieving competitive multiclass accuracy.Researchers have explored techniques to enhance the performance of these algorithms further [21, 54] . Ali et al. [3] developed a LSTM-GRU model, combining deep learning and graph analytics, which exhibits state of the art performance over a six class hate speech dataset.The Transformer architecture [67] , a highly effective generative sequential encoder commonly utilized in language modelling and recommendation tasks [47, 66] , has revolutionised text classification tasks. This is attributed to its capacity to capture relational information using self-attention mechanisms. Salminen et al. [61] focused on detecting hate across multiple platforms and found models with BERT features superior. Geet D'Sa et al. [21] conducted hate speech classification using word embeddings and Deep Neural Networks (DNN), with the fine-tuning approach using BERT achieving significant improvements. Caselli et al. [17] introduced HateBERT, a retrained BERT model that outperformed the base BERT model in detecting abusive language. 
methodology: 
 Problem Statement: We first define the problem as a text classification task with a dataset 𝐷 = (𝑥 1 , 𝑦 1 ), (𝑥 2 , 𝑦 2 ), ..., (𝑥 𝑛 , 𝑦 𝑛 ) where each datapoint contains a text 𝑥 and a corresponding true label 𝑦. The discrete action 𝑦 taken by the model is the classification of the data point into the set of classes. Each true label 𝑦 belongs to a finite set of classes 𝐶 = 1, ..., 𝑁 where N is the number of classes.The core idea of our methodology is to improve upon classification performance when solely using raw datapoint text. Due to class ambiguities, nuanced behavioural multi-class classification often leads to erroneous decisions. We have developed an end-to-end classifier which uses additional signals alongside text when classifying datapoints, thus improving classification performance. Instead of relying solely on the raw datapoint text input we also calculate numerical features, for example the measurement of the separation between various classes and the text input. These signals are then combined and tokenized before being passed to a fundamental transformer model (e.g., BERT).To integrate the varied signals, we harness the capabilities of the Decision Transformer Framework [19] . The decision transformer adeptly consolidates the information derived from the assorted rewards, facilitating a comprehensive analysis and well-informed decisions regarding classification. This approach successfully navigates the intricacies present within multi-class classification tasks, adeptly accommodating the subtle distinctions and resemblances among the different classes. A visual representation of our proposed architectural framework is presented in Figure 1 . Given a piece of text, we compute the distances between the text and each class. We then combine these singals and the raw datapoint text using the decision transformer framework. The decision transformer framework uses a base classifier (say BERT), to produce an output from the combined input. As shown in Fig. 1 (Causal transformer), the decision Transformer layer takes the stacked input of text and reward features and produces a classification output. The output of the decision transformer layer then subsequently passes through several linear transformations before being classified. Ultimately, the culmination of the process yields a classification output determined by a confluence of datapoint text and rewards. Gontier et al. [32] , utilise the decision transformer framework within a textbased game setting to train a reinforcement learning model. They incorporate additional signals computed from the input data with natural text elements of the game, using the decision transformer framework. We adopt a similar approach to incorporate our class based distance rewards with the text datapoints from the datasets.The model follows the standard architecture of a deep learning transformer model, shown in Equation 1, with the exception of the input 𝑥 which would normally be a textual embedding and is instead a combined text and reward feature embedding.EQUATIONTransformer model; where w, weight, and b, bias, are trainable parameters. 𝜓 is the Softmax activation function. Action, a, is selecting one of the N dataset classes.As depicted in Figure 1 , the text and reward features are combined before going through the tokenizer and decision transformer layer, which further goes through a number of transformations. The output of the transformer model is first passed through a dropout layer to help prevent overfitting. The dropout layer randomly sets a number of the input tensor elements to zero depending on a set probability. Next a linear layer, shown in Equation 2, is used to produce the output logits, a vector of raw predictions for each class within the task.𝑦 = 𝑥𝐴 𝑇 + 𝑏 (2)Linear layer equation; where x is the input, A is the weight, b is the bias, and y is the output.Finally, a softmax layer is then used to output the predicted class for the datapoint. The softmax function takes as input the values from the dropout layer and turns them into a probability distribution. The class with the highest probability within the softmax layer output is chosen as the predicted class.EQUATIONSoftmax layer equation; where Z is the output vector from the previous layers, 𝑍 𝑖 is the i-th element of Z, the value of e = ˜(2.718), and N is the number of classes.In order to remove cross-talk between classes, we design a reward modelling scheme. Primarily, we harness rewards as metrics of distances between the task classes and the current text embedding. As shown in Figure 1 , the input text is combined with reward features and then undergoes an embedding procedure (e.g., BERT), followed by the transformer model's output classification. Although the Decision Transformer paper [19] , which formed the foundation for this work, utilised the GPT-2 model [59] , we ultimately decided to use the BERT model [24] as the underlying transformer model within our research. This was due to BERT's superior performance in initial experiments, a decision reinforced by the findings of the experiments within this paper (see Tables 4 and 5 ). Rewards can be modelled by various techniques tailored to suit distinct problem tasks, and we have experimented with different schemes, emphasizing the diversity of our approach.Reward Function: The reward functions are instantiated as set of distances between mean class embeddings, formed from class sets, and the embedding of the text to be classified. These distances direct the classifier in making the correct decision. We explored numerous variations of reward functions based on distances during our experiments (section 4). In order to calculate the reward function, the cosine similarities between the text input embedding 𝑒 𝑖 and each mean class embedding ȳ are calculated as shown in equation 5 and the dashed line box of figure 1 . Next these cosine similarities are normalised and subsequently scaled from 1 to 100. We took this decision following research published by Wallace et al. [69] , who investigate various NLP model's understanding of numbers. They find that transformer models do capture numerical features but some, notably BERT, struggle with decimal numbers. A combination of these similarities is then returned as the reward, this then consequently forms part of the model input.We adopted the cosine similarity metric for our investigation to compute class distances, exploring two distinct methods. The first method entailed calculating the mean vector for each of the class clusters (equation 2) and gauging the cosine similarities between these mean vectors and text embedding. Conversely, the second method involved computing the cosine similarity between each possible pair of text embeddings of given text and elements of each class, then calculating an average cosine similarity. Upon evaluation, the first approach was deemed more efficient, thus becoming the chosen method implemented within our model.As a basis for the reward function mean text embeddings ȳ for each class have to be calculated, these are then used in the reward function as described below. The mean text embeddings are calculated as follows where 𝑦 𝑖 are individual text embeddings within each class.EQUATION)EQUATIONLoss for the model is calculated using cross-entropy loss as follows:EQUATIONfor N classes in the dataset, where 𝑡 𝑖 is the true label and 𝑝 𝑖 is the softmax probability for the, 𝑖 𝑡ℎ class.The prevailing datasets in the realm of conflict classification predominantly concentrate on overtly antagonistic manifestations of adverse interactions [23, 39, 31] , thus disregarding the complete spectrum of conflict. This constraint impedes our comprehension of these interactions' repercussions on users, particularly the potential harm inflicted on vulnerable users such as children. It becomes imperative to possess the means and mechanisms for identifying more expansive variants of conflict. In our research, we employ a more all-encompassing dataset of Facebook comments, affording an initial opportunity to scrutinize more nuanced manifestations of conflict.This conflict dataset was compiled by applying hate netnographies [42] . These netnographies facilitated the identification of six discrete categories of conflicts. These categories were deduced from a sixteen-month netnographic investigation across four online brand communities [14] . Throughout this investigative period, the researchers meticulously examined and classified many consumer conflicts, aiming to comprehend the diverse manifestations of such conflicts. The researchers adopted a dual-coding methodology to uphold the integrity of the annotation procedure, involving the active participation of two social science researchers. The initial phase encompassed the first researcher's deductive identification of incidents involving consumer conflicts. An independent analysis of the data was then undertaken by a second researcher. Both researchers subsequently engaged in a comprehensive assessment of the reliability and applicability of their respective analyses. This deliberation was accompanied by extensive discussions to resolve any divergence in their interpretations. This rigorous and meticulous process culminated in identifying and classifying six distinct categories of conflicts, namely 'Teasing', 'Criticism', 'Sarcasm', 'Trolling', 'Harassment', and 'Threats'. Final conflict dataset statistics shown in Table 1 .  
implementation and baselines 4.1 implementation: 
 All models were trained over four epochs with a learning rate of 2e-5 and the same Cross Entropy Loss Function from Pytorch, 𝑡𝑜𝑟𝑐ℎ.𝑛𝑛.𝐶𝑟𝑜𝑠𝑠𝐸𝑛𝑡𝑟𝑜𝑝𝑦𝐿𝑜𝑠𝑠 [20] . The Dropout [25] , Linear [48],and Softmax [64] layers also use the corresponding modules from the Pytorch library. For the Dropout Layer we set p=0.2. For the Linear layer, the 𝑛𝑛.𝐿𝑖𝑛𝑒𝑎𝑟 module takes two parameters; 𝑖𝑛_𝑓 𝑒𝑎𝑡𝑢𝑟𝑒𝑠, the number of input features, and 𝑜𝑢𝑡_𝑓 𝑒𝑎𝑡𝑢𝑟𝑒𝑠, the number of output features. For a classification task the number of output features is equal to the number of classes. We optimise models using AdamW [49] . For the baseline models, all hyperparameters were used from the published papers, trained using the same setup and fine-tuned on the same training, validation, and test splits from each dataset. The train, validation, test splits for all datasets were 80%, 10%, and 10% respectively.In order to evaluate classifier performance we used Accuracy, F-1 Score, Recall, and Precision. Due to the imbalanced nature of the conflict dataset, it is important to evaluate using both F1-Score and Accuracy. In order to validate the significance of our results we conducted statistical testing for the model classification performance.The statistical test we chose is the paired two-tailed t test. To get the performance metrics for each model we performed 5 K-fold cross-validation and took the average results. For each dataset in experiment two we calculated the t value between the Decision Transformer model and the base BERT model. The t-value was then used to calculate a p-value which was evaluated against alphas of 𝛼 = 0.05 and 𝛼 = 0.10.The following baselines were used:• BERT: due to proven performance in classification tasks, especially within social media text problems [71, 34] . For the BERT model, we used the BERT-Base uncased pre-trained model with 12 layers, 12 heads, 768 hidden size, and 110M parameters. • Flan-T5: A state of the art generative language model which can be fine tuned for text classification. Edwards and Camacho-Collados evaluate Flan-T5 for a multitude of text classification problems, showing strong performance metrics. [26] . We use the Flan-T5 base model with 248M parameters uploaded to the Huggingface repository by the google team [36] . [30, 7] . This was particularly relevant for our study due to the relatively small size of the conflict dataset. By testing our model on additional datasets, we demonstrated its ability to generalize and showcased the validity and robustness of it's performance.4.4.5 Experiment Three, investigating R.Q.3, focused on studying the reward function within the conflictDT model using the conflict dataset. The main challenge in classifying social media data sets is class crosstalk, notably exacerbated due to shared properties among classes for example, sarcasm, teasing, and trolling. Our approach in selecting individual reward functions involved a thorough analysis of distinct properties within the conflict dataset, specifically addressing existing misclassification patterns. We conducted a series of tests with multiple reward functions, the goal was to examine the impact of the reward function on overall classification performance. Showcasing the novelty and flexibility of the reward functionality within the model, which allows the prioritization of different behaviours and aspects within the classification problem. In order to further analyse the performance of the BERT and ConflictDT models we also include a breakdown of class performance within the conflict dataset. The reward function was varied while keeping all other parameters constant. We tested five different reward functions: (i) distance between the text embedding and all classes (equation 2); (ii) the distance between the text embedding and lesser and more extreme forms of conflict; (iii) distance between the 'Harassment' class and the text embedding; (iv) the distance between the text embedding and all classes with sequential functionality; (v) no reward with sequential functionality. These reward functions were selected based on our knowledge of the dataset and analysis conducted in the experiments one and two. The first reward function, 'distance between all classes', aimed to prioritize separating all classes within the dataset and could be applied to any classification task. The second reward function, 'distance between lesser and extreme conflict groups', aimed to exploit common characteristics within these two groups, as identified during dataset analysis. The 'Harassment' based reward function was derived from the initial results of our model, which indicated that the 'Harassment' class datapoints were being frequently misclassified into other classes and vice versa (Fig 3 ). Consequently, we sought to demonstrate the model's ability to prioritize different classification behaviours by designing a reward function to address this trend. The final two variations of conflictDT feature sequential modelling. The Decision Transformer paper that inspired this work [19] utilise transformer models as part of sequence modelling problems. We sought to map this same framework to text classification by mapping states to sentences within the datapoints. We divided the datapoints into sentences, with each state consisting of the previous states plus the next sentence. So the first state would contain the first sentence, the second would contain the first two sentences; and so on. The input for these sequence modelling classification models would then be a combination of the current state text, the output of the reward function for that text state, and the previous classification taken by the model. The model would then provide a classification for each state, with the final state being the entire text datapoint. This sequence modelling approach aimed to exploit the sequential In order to gain an insight into common themes of misclassification we conduct thematic analysis, a popular and recognised technique within social sciences used to complement quantiative approaches, formally defined by Braun and Clarke [13] . Thematic analysis is a method used to make sense of human communication content, in our case online comments, to identify themes or patterns that emerge. We follow the standards set out by Braun and Clark [12] which feature 6 steps; Familiarization of data, Generation of codes, Combining codes into themes, Reviewing themes, Determine significance of themes, and Reporting of Findings. We follow an inductive coding approach where one coder annotates the data, then a second coder reviews the first coders identified codes, patterns, and themes. The two researchers then enter a discussion before finalising the codes, patterns, and themes. The analysis concludes by reporting the resulting themes, definitions, descriptions, and sample comments in order for others to replicate and understand the results. 
results and discussion 5.1 experiment one -analysing multi-class conflict dataset characteristics and robustness: 
 The main contribution of experiment one is analyzing the novel conflict dataset encompassing a range of negative behaviours. As highlighted by Bianchi et al. [9] , there is a scarcity of research on nuanced hate and conflict, thus, this paper fills an evident gap in existing work. The conflict dataset is grounded in established marketing theory, employing social science researchers with expertise in hate and conflict to annotate the data.Due to our task's complex and real-life nature, we investigated similarity between the six classes using a heatmap, Figure 2 , and observed an average similarity of (0.32) between classes. Whilst similarities between some conflicts are low e.g. 'Threats' and the other classes, there is less distinction between others such as 'Criticism' and 'Harassment' which had a high level of similarity (0.71). This similarity poses a challenge for classification, particularly given the small dataset. Higher similarity between classes results in greater difficulty for classifiers as the classification problem is more ambiguous. Brevity of data is another aspect to consider in this context, as such we conducted an analysis of textual features (Table 2 ). With the character length and number of words means across all classes being just 147 characters and 26.85 words respectively. Although research has established correlation between shorter texts and reduced performance [65] , the conflict dataset's short length accurately reflects the domain's characteristics and the classification task. Nonetheless, developing robust methods to effectively handle short texts and overlap between classes is crucial.By generating a class similarity matrix we delve into the conflict dataset composition beyond class size, gaining insights which further enrich dataset evaluation and development of our custom reward function. For instance, we can observe that, on average, milder conflicts exhibit greater similarities than severe conflicts. Furthermore, we can identify a significant level of similarity between the 'Criticism' and 'Harassment' classes. This could be due to links between the behaviours, previous works [40, 38] have specifically investigated crossover between the two; how actors within interactions can have different perceptions of criticism and harassment, and how criticism develops into harassment. These findings contribute to shaping our reward functions in the context of experiment three. However, despite the merits, the dataset does have limitations. While dataset size is comparable to other works [62] , it remains relatively small compared to other extensive datasets available [23, 68] . However, SOTA deep learning models have demonstrated impressive performance working with small datasets [62, 17] . In accordance with limited data availability, the conflict dataset used in this paper is also relatively small. Another drawback is class imbalance; due to the nature of social media data, not all classes were equally represented.As can be seen in Table 4 , the conflictDT model, with a reward function of distance between all classes, outperforms other models in F1 score on the Founta dataset [31] . We achieve superior performance to BERT in F1 score by 1% , whilst also significantly outperforming GPT-2 by 5% in F1 score. The best performing model "everyone in every country has a right to their own opinion. I'm not sure why it matters what country, it's their opinions and their beliefs. No one is asking you to agree but respect the fact they are entitled to their opinions and beliefs. "A misclassification that occurs when the meaning of a message changes when taken out of context. Context dependency is a common issue identified by language researchers when studying offensive/impolite communication. The type of behaviour exhibited in a datapoint depends on the context of the prior comments. When classifiers are utilising single comment datapoints this context is not present, as opposed to the human coders who had access to the whole conversation when conducting netnographies."Picture that says: it's a joke not a dick, don't take it so hard. " "If Andy Dwyer wasn't likable. "A misclassification that occurs when a message fails to convey that it was meant in humor and/ or was good vs bad-natured.Linguists have long recognised the lack of clarity inherent to humor as a quality on which humor often relies. Humour has been recognised as a particularly challenging area of NLP. Humor can be taken two ways and is subjective meaning the dominant type of humour behaviour is often ambiguous within datapoints."The Not So Fresh Princess of Stale Air. " "Keep checking on Arsenal every champions league game mate ! Mu won't be there! Arsenal are here to entertain! unlike your confused club!" and a p-value of 0.15. Although this result is not significant using thresholds of either 𝛼 = 0.05 or 𝛼 = 0.10, the ConflictDT was still tested using 5 k-cross fold validation and thus still shows merit in it's performance.The results across three popular datasets show our conflictDT model performs well within hate and conflict text classification. With regards to the F1 score metric our model outperforms SOTA models on two datasets whilst achieving marginally better performance on the third. By showing that our model produces robust results whilst also generalising across datasets, we increase the reliability of our results. This, therefore, mitigates the shortcomings of the conflict dataset, which suffers from class imbalance and data size. In addition, it also shows the adaptability of the reward functions for new data scenarios.Reward Functionality Performance and it's effect on individual class performance.Experiment three delved into a comprehensive examination of reward functionality in our conflictDT model. within the conflict dataset, quantified in Experiment One Table 2 .The additional data gained at each timestep may simply not be enough to aid the model, and instead causes confusion within the signals.Examining the heatmaps for the BERT and ConflictDT models over the six class conflict dataset, we observe that ConflictDT obtains higher true positives in Teasing, Criticism, and Trolling. The same true positives in Sarcasm, and lower true positives in Harassment. Additionally, the ConflictDT model results in a reduction in all other classes being misclassified as Harassment, while Harassment is more frequently misclassified into Sarcasm and Trolling.The key finding of this experiment is that the reward functionality empowers us to modify model behaviour. This distinctive capability to leverage prior knowledge and counteract specific trends in model classification is a significant advantage. Both general rewards, such as the distance between all classes, and rewards that target specific high misclassifications, can lead to improved model performance. However, it should be noted that specific rewards, such as the distance between less severe and more extreme classes, can result in decreased performance. Considering the earlier dataset analysis, this outcome was unexpected as this reward function was anticipated to enhance performance. We had theorised that promoting distance between the two class groups would reduce misclassification between them. This decrease in performance may be due to the ambiguity of classes within each group or the differences between the groups not being significant enough.The thematic analysis employed produced three main misclassification themes which we have called Linguistic Fluidity, Context Dependency, and Humour Ambiguity. These themes alongside their definitions, descriptions, examples, and frequency can be seen in table 3 . The theme of linguistic fluidity encompasses the fluid or blurred boundaries between class behaviours. Although datapoints tend to have a dominant behaviour, they can contain aspects of multiple class behaviours. This presence of ambiguous behaviours has been identified in other works. For example, both Jhaver et al. and Kim et al. [38, 40] identify crossover between Criticism and Harassment. They discuss how Criticism can develop into Harassment and how often the true identify of the behaviour is subjective. Additionally, we can see this theme emerging within hate terminology. Fortuna et al. [29] discuss how terminology differs between research papers within the hate domain, leading to ambiguity between behaviour classes in different datasets and misinterpretation of the identity of behaviours within the hate domain.The second theme, Context Dependency, relates to those datapoints where the identification of the behaviour class partially or majorly relied on context which was unavailable to the classifier. Whilst single social media datapoints frequently contain obvious negative behaviours they are often contained within or associated with a larger 'chain' or 'thread' of comments. Therefore, the behaviours prescribed to the individual datapoints are related to the other activity within those chains. As this information is unavailable to the classifier it leads to misclassification as the correct behaviour is context dependant. This theme has been identified and attempts made to address it by a plethora of researchers within the classification domain [58, 22, 60, 18] , with many seeking to develop models which consider entire social media conversations within classification problems.The final theme, Humour Ambiguity, relates to the difficulty of NLP models to identify specific forms of humour. Humour has been recognised as a particularly challenging area of NLP. Humour is largely subjective and often relies on subtle cues or inside knowledge. Take for example the first humour ambiguity datapoint in table 3, 'The Not So Fresh Princess of Stale Air. ' which belonged to the 'Trolling' class but was misclassified as 'Harassment', the difficulties in identifying this datapoint are two-fold. First the model has to understand that the comment has a humour aspect and is not just an insult, secondly the understanding that the datapoint references a popular comedic show from the 1990's. Whilst a human could reasonably understand that the comment has a humourous aspect, the model struggles with these nuanced factors. We can see the pattern develop in the second example of humour ambiguity where the nuance of humourous teasing is dependent on knowledge of football clubs and the banter which exists between the fans of said clubs.In future work, we will expand the dataset size. While we have taken steps to mitigate the impact on the significance of model performance by testing on other popular datasets, increasing the scale of the conflict dataset would enhance the reliability and robustness of our results. Additionally, there is room for further exploration of reward functions. Our experiments focused on the distance between class text embeddings, but future reward functions could encompass other aspects, such as distance between datapoint text embeddings within classes or similar emotions and sentiments within classes. Lastly, research could focus on the sequential modeling we briefly explored in experiment three, the decision transformer work by Chen et al. [19] showed sequential modelling to achieve great success in other IR tasks. Given the sequential nature of text, the decision transformer framework could be exploited further, perhaps considering alternative representations of states within the text, such as individual words, different groups of words, or even paragraphs. Although we provide a qualitative analysis of our models misclassifications using a popular and well recognised social science technique there is potential to perform a quantitative explainable AI experiment. This would aid in understanding the role that the reward functionality plays within the model. Finally, a case study investigating the use of this model within a simulated real life scenario could be conducted, investigating the feasibility of the model as a tool for use within social media platforms. Such a case study would further demonstrate the potential important impact of a model capable of identifying a range of behaviors whilst highlighting responsible AI practices. 
conclusion: 
 We successfully developed and evaluated a novel social media conflict classification system. The novelty of our research lies in our innovative approach to modeling multi-class classification. Specifically, we have devised a novel reward scheme that extracts nuanced signals essential for addressing the complexities inherent in multi-class classification scenarios. Leveraging the decision transformer architecture, we effectively integrate these classification signals, thereby enhancing the classification process. In addition, our approach is applicable to various problem domains, beyond the scope of hate and conflict analysis. The use of a dataset covering a spectrum of social media conflict, including less extreme forms, fulfils a gap in the current literature which predominantly focuses on extreme forms of hate. A quantitative analysis of this conflict dataset, examining class characteristics, class similarity, and individual class classification performance contributes to the robustness of the work. Our model significantly outperformed state of the art models on two multi-class conflict and hate datasets, and achieved non-significant outperformance on the third. Finally, we conduct a qualitative analysis of model misclassifications, employing thematic analysis to identify trends and patterns within misclassifications. With the successful results achieved, the classification system and custom dataset can serve as the foundation for further exploration into social media conflict. 
 
 Appendix: https://www.rtor.org/2019/10/07/ways-a-toxic-environment-can-be-detrimental-toyour-mental-health/ 


Decision: This is a meta-review. This paper addresses the problem of conflicts on social media. The authors propose a multi-objective classification model to identify both subtle forms and openly hostile forms of conflict. The reviewers all agree that the paper is of sufficient quality to be accepted, although they also list a number of weaknesses. Especially reviewer NFzY and WPug make some concrete comments that I suggest the authors to address in their revision. Overall, the paper addresses a relevant problem, and the methodology is sound. The annotated dataset is an important contribution and the authors make good suggestions for future work. The results are not all very convincing, but that is not a reason to reject the paper. (Note: The abstract has a placeholder not-functioning anonymous git URL; please replace by an actual URL in the revision.)

Reviews: review: main_review: main_review: The paper would benefit greatly from providing the code and dataset via an open science platform such as an anonymous GitHub repository. This would facilitate the review process and enhance reproducibility.

The authors spend considerable effort explaining basic concepts like softmax and linear layer equations, which do not add significant value to the paper.

The reward function section lacks sufficient explanation. For example, the normalization and scaling of cosine similarities from 1 to 100 is not adequately justified.

The rationale behind the chosen research questions is not well-organized, and the repetition of experiments in sections 4.4 and 5 makes the paper repetitive.
main_review: main_review: Overall, the work presents novel contribution to the area and should be considered for acceptance. The experimental section supports the premise that the proposed model can achieve superior performance compared to baseline approaches. However, that performance increase is somewhat marginal and not statistically significant (alpha = 0.1) under all of examined metrics. The inclusion of the compiled multi-class conflict dataset offers an additional intuitive way of evaluating the model.

Minor comments:
1) The related work section could be noticeably extended (perhaps with additional material related to RQ4)
2) Please capitalize all cross-references to Tables, Figures etc. in the main text.
main_review: main_review: I like the hypotheses and the presentation of how they work. I think the motivation for the new classification approach (incorporating whether a text roughly matches known examples of the classes) is good.

However, I found the presentation a little bit difficult to follow at times. The heat maps, in particular, did not seem very useful toward understanding things. That may be because in the end, the new approach resulted in modest to low gains in effectiveness, meaning the heat maps were less useful because there was less to show. It may also mean that this submission is trying to solve a problem that is not actual present in the data in the right way.


Here are some very small issues that should be easy to fix or at least easy to decide whether to fix or ignore:

* Introduction. The sentence  starting "The lesser behaviours" is not a sentence: there is no main verb.
* Maybe this is a cultural thing, but normally one uses a colon (:) rather than a semicolon (;) to introduce a list. So in the intro, "behaviours encompassing: anonymity, the absence of..." 
* In the contributions, you "formulate" and "evaluate" rather than "formulated" and "evaluated" for parallelism with the others.
* I don't get what DT in conflict DT means. It'd be nice if you tossed in a quick explanation.
* In 3.1, singals $\longrightarrow$ signals
* In 4.2, rather than "5 K-fold" I think you want "5-fold" since the other looks suspiciously (and implausibly) like "5000-fold"
* Also in 4.2, you are suddenly talking about "experiment two" which we have never heard of previously. Needs some explanation.
* In 4.4.5, "work [19] utilise" $\longrightarrow$ "work [19] utilise**s**"
* The 5.3 heading, it's $\longrightarrow$ its
* In 5.3, the paragraph starting "Examining the heatmaps" needs a pointer to Figures 3 and 4.
* In 5.4, I suspect that: true identify $\longrightarrow$ true identity
* In conclusion, "fulfills a gap" $\longrightarrow$ "fills a gap"
main_review: main_review: The paper was easy to follow and the problem is important and very relevant. The analysis that the authors do in the results section is thorough and interesting. However, the improvement is not big which is the main limitation of the paper.