Aggressive Comments Research Articles

Использование метаграфемных средств для выражения эмоций в интернет-комментариях

Оur research object is online comments to a text about reducing pension expenses and a sharp reduction in the number of pensioners in Russia. The text had a strong conflict potential due to the manipulative techniques used by the author; it caused a large number of aggressive comments from readers – people of retirement and pre-retirement age. The study subject is metagraphematic (singragraphemic and supragraphemic) means used by the authors of Internet comments as negative emotions expressing means along with verbal means of speech aggression. The following text analysis methods were used: formal, logical, semanticand syntactic analysis method. Features of metagraphemic means use in online comments were identified. In singraphemics this is the use of non-regulated by the Russian grammar rules punctuation marks and their homogeneous and heterogeneous combinations,etc. In supragraphemics – unmotivated and grammatically unsound use of uppercase and lowercase letters, as well as the use of specific emoticon signs indicating negative emotions experienced by the author of the comment. The reasons for this metagraphematic means usage in the comments may be the following: communicants’ state of emotional tension caused by the publication content and leading to an increase in the pace of computer typing; their age and the insufficient proficiency level of the typing techniques as well as the liberalization of language norms in Internet communications and poor grammatical rules knowledge by communicants. Our research showed that metagraphematic means are actively used in Internet comments to express the communicants’ emotions.

Flip that coin: Barriers, barrier‐breakers, and full‐adoption of digital payment methods

AbstractThe purpose of this study is twofold: to examine the mediating effect of a number of barrier‐breakers on the relationship between barriers and the intention to fully adopt digital payment methods; and to identify adopters‐resisters' commenting behaviour when facing a full‐adoption scenario. Mixed methods were applied. The PROCESS macro method was used to analyse 388 survey responses and test the mediating effects. The main study reveals that the privacy, security, and access barriers can be reduced by increasing the credibility and usefulness barrier‐breakers, respectively. However, the impersonalisation barrier is found to be unaffected by the social influence barrier‐breaker. Based on 91 collected and analysed comments, the netnographic‐based follow‐up study identifies social media commenting behaviours caused by a major bank's decision to stop handling cash. Among the more frequent such behaviours are boycotting and aggressive comments.

Can Social Media Be a Place for Women? Effects of Aggressive Comments on User Engagement in Collective Action for Gender Equality in China

Can Social Media Be a Place for Women? Effects of Aggressive Comments on User Engagement in Collective Action for Gender Equality in China

A Multilayered Preprocessing Approach for Recognition and Classification of Malicious Social Network Messages

The primary methods of communication in the modern world are social networks, which are rife with harmful messages that can injure both psychologically and financially. Most websites do not offer services that automatically delete or send malicious communications back to the sender for correction, or notify the sender of inaccuracies in the content of the messages. The deployment of such systems could make use of techniques for identifying and categorizing harmful messages. This paper suggests a novel multilayered preprocessing approach for the recognition and classification of malicious social network messages to limit negative impact, resulting in fewer toxic messages, scams, and aggressive comments in social media messages and commenting areas. As a result, less technical knowledge would be required to investigate the effects of harmful messages. The dataset was created using the regional Lithuanian language with four classes: aggressive, insulting, toxic, and malicious. Three machine learning algorithms were examined, five use cases of a multilayered preprocessing approach were suggested, and experiments were conducted to identify and classify harmful messages in the Lithuanian language.

A Group Decision-Making Method Based on the Experts’ Behavior During the Debate

Debate is a process consisting in arriving at a reasoned opinion on a proportion in which individuals must be truly capable of defending their own judgments. It has been used within group decision-making (GDM) problems to help experts make better decisions. However, whether experts engage in a vigorous debate, it can result in the use of aggressive language that may diminish consensus, which is the major objective of GDM. To avoid it, we present a novel method for GDM problems that can identify aggressive comments during the debate by incorporating a classifier based on sentiment analysis techniques. According to the information extracted during the debate, two procedures are developed to assign weights to the experts, which are used to introduce two new consensus measures and to make the final decision. Unlike the existing GDM methods, this new one can take advantage of the information extracted during the debate (i.e., experts’ behavior) throughout the decision process, making it in rapport with real-world GDM processes.

Even pseudonyms and throwaways delete their Reddit posts

Concerned researchers of user-generated content might want to avoid using, citing, or quoting sensitive content likely to be deleted by their authors, even when pseudonymous or using one-time “throwaway” accounts. At Reddit, how many authors actually delete their submissions, why, and are they concerned if their deletion end up elsewhere? I analyze the three most popular sensitive-topic subreddits (r/Advice, r/AmItheAsshole, and r/relationship_advice) and show that deleting submissions is common. Roughly half of submissions are deleted by their users, most within the first day and week. Interviews with 30 Redditors reveal that their motives for deletion include ensuring the “Internet doesn’t see them,” especially those who might “see it on my Reddit profile,” deciding their issue was resolved, receiving unhelpful or aggressive comments, and concluding their submission was no longer relevant. Most interviewees were not overly concerned about deleted submissions persisting elsewhere (e.g., social media, archives, and datasets) as long as it is not easily connected to their other activity or identity.

Finding Harmful Comments on Social Networking Sites Using NLP and Machine Learning Methods

The usage of violent language has significantly increased due to social media and networking. A key component in this is the younger generation. More than half of young people who use social media are affected by cyberbullying. Harmful interactions occur as a result of insults expressed on social net-working websites. These comments foster an unprofessional tone on the internet, which is usually un-derstood and mitigated through passive mechanisms and techniques. Additionally, the recall rates of current systems that combine insult detection with machine learning and natural language processing are incredibly poor. To establish a viable classification scheme for such concepts, the research ana-lyzes how to identify bullying in writing by examining and testing various approaches. We propose an effective method to assess bullying, identify aggressive comments, and analyze their veracity. NLP and machine learning are employed to examine social perception and identify the aggressive impact on in-dividuals or groups. The ideal prototyping system for identifying cyber dangers in social media relies heavily on an efficient classifier. The goal of the paper is to emphasize the critical role that learning strategies play in enhancing natural language processing efficiency.

Конфликт интерпретаций текста как следствие когнитивного диссонанса участников интернет-обсуждений

Latent cognitive dissonance in Internet disputers manifests itself as a conflict of text interpretations. This cognitive interpretation process of pre-textual cognitive dissonance often results in aggressive comments. The author analyzed interpretive Internet texts (comments) to reveal the cognitive patterns and prove the effect of pre-textual, i.e., cognitive, dissonance on the language and content of Internet comments. Addressee's external interpretive activity, represented in the text, is determined by internal cognitive processes, i.e., pre-textual presumptions and expectations. The study was based on the article entitled The people are multiplying "on earth", which was posted on a Runet information network resource on January 30, 2023. The article, which featured national demographic problems and possible solutions, yielded 189 comments that clearly illustrated the mechanism of conflict of interpretations. The methods of content and intent analyses revealed two cognitive patterns: the first one correlated with the intentional content of the trigger-text whereas the second pattern did not. Both patterns covered a number of cognitive models. The original text, subjectively interpreted by the addressee, triggered a wide range of comments that followed various cognitive patterns. As a result, the discussion unfolded as a conflict scenario, i.e., a struggle of attitudes and opinions, determined by the pre-textual cognitive dissonance of the disputers.

Psychometric Properties of the Moral Disengagement through Technologies Questionnaire (MDTech-Q) in a Sample of Chilean University Students.

In the virtual environment, hostile and aggressive comments that could negatively affect university students who often use different digital platforms are frequently observed, more than other age groups who have little or no supervision. In this sense, moral disengagement (MD) has been linked to different negative behaviors that manifest in physical interactions and which currently take place on the Internet, creating the need for instruments that specifically address MD online. The aim of this study is to adapt and validate the Moral Disengagement through Technologies Questionnaire (MDTech-Q) with Chilean university students. The sample comprised 527 university students (43.14% men, 56.86% women), with an average age of 22.09 years (SD (Standard Deviation) = 3.59) enrolled in 12 universities. First, a linguistic adaptation of the scale took place, and the surveys were applied considering ethical principles. Then, two confirmatory factor analyses (CFA) were performed, which considered four correlated factors, and provided satisfactory indices, agreeing with the original theoretical proposal, and demonstrating suitable reliability by internal consistency. In relation to the analyses of invariance according to sex and social media use, the MDTech-Q is stable up to scalar invariance. This study provides evidence of the psychometric quality of the MDTech-Q for its use on Chilean university students.

Xenophobia and Hate Speech towards Refugees on Social Media: Reinforcing Causes, Negative Effects, Defense and Response Mechanisms against That Speech

This study aims to recognize the possible role of social media in forming xenophobia and hate speech against Syrian refugees and to understand the reinforcing causes and negative effects of that speech on the refugees, as well as the refugees’ response mechanisms against that speech. A mixed approach was used in this study. In addition to a questionnaire tool that was used to collect data, focus group discussions were conducted to support the obtained data. The study used a statistically representative sample of Syrian refugees who live in Turkey and Jordan. The participants see the negative representation of their image via social media as leading to hatred towards them. The findings showed that there are some causes that reinforce xenophobic speech, such as otherization and demonization of refugees, by the negative representation and spreading of fake news on social media, which are published continuously. Moreover, gatekeepers have a role in promoting negative representation by approving xenophobic speech transmission without filtering or ethical control. The results also showed that the negative representation containing hate speech contributes to more negative effects on the refugees, such as psychological effects, as a sense of hatred towards refugees is created in the host country, and the effect of changing the feelings of the public from empathy to compassion fatigue concluding to hatred towards those refugees. Furthermore, the results showed that the Syrian refugees follow different defense mechanisms against xenophobia and hate speech against them published and circulated through social media platforms. The participants were divided into two main categories; the first one is those who prefer replying, not being silent, and defending the refugees, and the second category is those who prefer keeping silent. The first category of refugees stated that they do not accept keeping silent and they prefer to reply using logical, emotional, or defensive and offensive ways to bad comments against Syrian refugees in general. While the second category claimed that either they do not have time to reply or they sometimes agree that it was only a reaction to the behavior of some individual refugees. Some of them explained that they are afraid of being subjected to more bad comments and negative reactions against them. When they encounter aggressive comments and see that such views represent the majority, they prefer to keep silent to avoid being attacked. This is understood within the framework of the spiral of silence theory, where refugees hide their opinions, views, and preferences when they think that they fall within a minority group. They fear social isolation through social media.

Verbal Aggressiveness against Public Figures’ Language: An Analysis of Tweeps’ Comments on Twitter

Aggressive communicators take opportunities to attack the self-concept of others on Twitter. Among other Twitter users, public figures tend to receive more verbally aggressive comments. This study identified the forms of verbal aggressiveness contained in the tweeps’ comments and analyzed the underlying rationales of the tweeps to express such verbally aggressive behavior. From 120 tweeps’ comments analyzed, character attack was found in 10 tweeps’ comments (8.3 %), competence attack was found in 3 tweeps’ comments (2.5 %), physical appearance attack was found in 22 tweeps’ comments (18.3 %), the dominant kind of verbal aggressiveness—insult was found in 38 tweeps’ comments (31.8 %), the minor kind of verbal aggressiveness—malediction was found in 1 tweep’s comment (0.8 %), teasing was found in 33 tweeps’ comments (27.5 %), and profanity was found in 13 tweeps’ comments (10.8 %). Of all verbal aggressiveness forms, politicians received verbal aggressiveness in the forms of character attack, competence attack, physical appearance attack, insult, teasing, and profanity. Then, celebrities received all kinds of verbal aggressiveness. However, practitioners only received physical appearance attacks, insults, teasing, and profanity. Environmental influence, pseudonymity, and dissatisfaction became the primary rationales of the tweeps to attack the self-presentation of public figures on Twitter.

OPENING PANDORA’S BOX: A CRITIQUE OF PEER REVIEW PROCESSES

Publishing procedures in all scientific areas have been in constant flux to ensure articles’ formal unity and most importantly significant contribution to respective research fields. Scientists but more specifically higher education professionals across the globe have joined a race for “points” to warrant their standing in academic communities or to comply with promotion or tenure requirements. Publishing scientific/ academic work in high-ranking journals is today the norm in most universities worldwide which has imposed on editorial teams methods of selection relying almost exclusively on the authority of reviewers. This article will present a brief overview of recent concerns regarding the peer review practice in different publishing fields and the issue of less than collegial behaviours that have also emerged. The paper examines the importance of unbiased feedback of specialists which ensures the quality of published materials and highlights authors’ apprehension about bullying in peer review processes. The present critique will also mention the need for golden rules of conduct for peer reviewers and the necessity of editorial boards to supervise and address inappropriate aggressive comments from reviewers.

Verbal Aggressions Detection in Mexican Tweets

Verbal aggressions are a struggle that a great number of social media users have to face daily. Some users take advantage of the anonymity that social media give them and offend a person, a group of people, or a concept. The majority of proposals which pretend to detect aggressive comments on social media handle it as a classification problem. Although there are a lot of techniques to face this problem in English, there is a lack of proposals in Spanish. In this work, we propose using several Spanish lexicons which have a collection of words that have been weighted according to different criteria like affective, dimensional, and emotional values. In addition to them, estructural values, word-embeddings and one-hot codification were taken into account.

Contagion of Offensive Behavior for Transient Interactions in Online Commenting Platforms

ABSTRACT Online aggression is an increasingly significant problem in Internet-based communication systems (e.g., commenting platforms) with potentially negative effects on online participation and quality of online discourse. Similar to offline aggression, online aggression has been shown to be contagious and to spread through online platforms as a result of exposure to aggressive content. Among other demographics, young adults report experiencing online aggression at alarming rates. In this paper, we examine the short-term effects of exposure to different levels of online aggressive behavior, and investigate whether it leads to an increase in online aggression among 118 young adults. Using an emulated commenting environment, we found evidence for the contagious nature of aggressive commenting. Even when individuals knew comments were synthetic (and not generated by actual peers), exposure to higher levels of aggressive comments resulted in a statistically significant increase in user aggression. Surprisingly, our results show that anonymity neither increased nor decreased the aggression response, which could indicate a change in user behavior since the advent of Internet 2.0 technologies or qualms related to experimenter-subject anonymity. Consequently, the results in this paper support the hypothesis that even in transient online interactions, young adult participants mimic their peers in an attempt to minimize social stress. Moreover, this mimicking occurs even when the participants know they are participating in an artificial social system.

An Examination of Verbal Aggression in Politically-Motivated Digital Discourse

Verbal aggression in digital discourse has emerged as a substantial focus of interest for scholars wishing to examine how conflicts begin, unfold and end on Twitter, YouTube, blogs, and WhatsApp. However, despite all the scholarly attention that Facebook has received since its origin, verbal aggression on Facebook is still relatively under-researched. This paper examines verbal aggression online in the political discourse of Congo-Brazzaville Facebook users. More specifically, quantitative along with qualitative analyses of a dataset of 9,330 Facebook comments were jointly carried out. One critical finding is that explicit aggressive comments are overwhelmingly pervasive and on the increase, pointing to the conclusion that historical, social, sociolinguistic, and political factors are to a larger extent instigators of verbal aggression on Facebook. Furthermore, users employ different strategies to express or intensify verbal aggression, favoring explicit expressions of aggression such as insult and abuse over others like teasing.

Transformer network-based word embeddings approach for autonomous cyberbullying detection

PurposeNowadays people are connected by social media like Facebook, Instagram, Twitter, YouTube and much more. Bullies take advantage of these social networks to share their comments. Cyberbullying is one typical kind of harassment by making aggressive comments, abuses to hurt the netizens. Social media is one of the areas where bullying happens extensively. Hence, it is necessary to develop an efficient and autonomous cyberbullying detection technique.Design/methodology/approachIn this paper, the authors proposed a transformer network-based word embeddings approach for cyberbullying detection. RoBERTa is used to generate word embeddings and Light Gradient Boosting Machine is used as a classifier.FindingsThe proposed approach outperforms machine learning algorithms such as logistic regression, support vector machine and deep learning models such as word-level convolutional neural networks (word CNN) and character convolutional neural networks with short cuts (char CNNS) in terms of precision, recall, F1-score.Originality/valueOne of the limitations of traditional word embeddings methods is context-independent. In this work, only text data are utilized to identify cyberbullying. This work can be extended to predict cyberbullying activities in multimedia environment like image, audio and video.

Multi-modal cyber-aggression detection with feature optimization by firefly algorithm

Aggressive comments containing offensive images and inappropriate gesture signs together with textual comments have grown exponentially in the recent past on social media. These aggressive contents on social media are affecting the victims negatively causing fear, stress, sleeping problems and even suicide in some cases. Since social media contents are unmoderated, a technical solution with the characteristic of having automatic flagging of these contents considering the text and images together is highly needed. This article presents a deep learning and binary firefly-based optimization-based model to classify the social media posts into high-aggressive, medium-aggressive, and non-aggressive classes. The proposed model considers both text and images together to evaluate the aggression level of a post. In this model, the image features of the posts are extracted using pre-trained VGG-16 model, whereas the textual features are extracted using a three-layered convolutional neural network in parallel. The image and text features are then combined to get a hybrid feature set which is further optimized using a binary firefly optimization algorithm. Our proposed model improves the results by 11% in terms of the weighted F1-score with optimized features by binary firefly algorithm over non-optimized features.

Multi-modal aggression identification using Convolutional Neural Network and Binary Particle Swarm Optimization

Aggressive posts containing symbolic and offencive images, inappropriate gestures along with provocative textual comments are growing exponentially in social media with the availability of inexpensive data services. These posts have numerous negative impacts on the reader and need an immediate technical solution to filter out aggressive comments. This paper presents a model based on a Convolutional Neural Network (CNN) and Binary Particle Swarm Optimization (BPSO) to classify the social media posts containing images with associated textual comments into non-aggressive, medium-aggressive and high-aggressive classes. A dataset containing symbolic images and the corresponding textual comments was created to validate the proposed model. The framework employs a pre-trained VGG-16 to extract the image features and a three-layered CNN to extract the textual features in parallel. The hybrid feature set obtained by concatenating the image and the text features were optimized using the BPSO algorithm to extract the more relevant features. The proposed model with optimized features and Random Forest classifier achieves a weighted F1-Score of 0.74, an improvement of around 3% over unoptimized features.

Analysis of Parent Discipline as Predictor of Aggression Across Generations

The researchers hypothesized that these aggressive comments could be the result of experiencing corporal punishment from their parents. This paper studies whether parent discipline predicts aggression across generations by utilizing causal-comparative research design. Based on the results, punishment (N=1045) was the most received parent discipline among the respondents (N=2600). Results also showed that females (Mdn=7.30) were more likely to receive punishment than males (Mdn=6.94). Furthermore, there is also a significant difference in the level of aggression between generations (p<0.001) with Generation Z having the highest level compared to Generation X and Y. Lastly, a multiple regression was run to predict Aggression from generation, sex, parent discipline, rate of punishment, frequency of punishment, parent discipline satisfaction, and agreeing to kill criminals. Stepwise analysis filtered the independent variables and included Frequency of Punishment, Satisfaction of Parent Discipline, and Generation.

Detecting and visualizing hate speech in social media: A cyber Watchdog for surveillance

The multi-fold growth of the social media user-base fuelled a substantial increase in the amount of hate speech posts on social media platforms. The enormous data volume makes it hard to capture such cases and either moderate or delete them. This paper presents an approach to detect and visualize online aggression, a special case of hate speech, over social media. Aggression is categorized into overtly aggressive (OAG), covertly aggressive (CAG), and non-aggressive labels (NAG). We have designed a user interface based on a web browser plugin over Facebook and Twitter to visualize the aggressive comments posted on the Social media user’s timelines. This plugin interface might help to the security agency to keep a tab on the social media stream. It also provides citizens with a tool that is typically only available for large enterprises. The availability of such a tool alleviates the technological imbalance between industry and citizens. Besides, the system might be helpful to the research community to create further tools and prepare weakly labeled training data in a few minutes using comments posted by users on celebrity’s Facebook, Twitter timeline. We have reported the results on a newly created dataset of user comments posted on Facebook and Twitter using our proposed plugins and the standard Trolling Aggression Cyberbullying 2018 (TRAC) dataset in English and code-mixed Hindi. Various classifiers like Support Vector Machine (SVM), Logistic regression, deep learning model based on Convolution Neural Network (CNN), Attention-based model, and the recently proposed BERT pre-trained language model by Google AI, have been used for aggression classification. The weighted F1-score of around 0.64 and 0.62 is achieved on TRAC Facebook English and Hindi datasets while on Twitter English and Hindi datasets, the weighted F1-score is 0.58 and 0.50, respectively.