Social media is a vital means for information-sharing due to its easy access, low cost, and fast dissemination characteristics. However, increases in social media usage have corresponded with a rise in the prevalence of cyberbullying. Most existing cyberbullying detection methods are supervised and, thus, have two key drawbacks: (1) The data labeling process is often labor-intensive and time-consuming; (2) Current labeling guidelines may not be generalized to future instances because of different language usage and evolving social networks. To address these limitations, this work introduces a principled approach for unsupervised cyberbullying detection. The proposed model consists of two main components: (1) A representation learning network that encodes the social media session by exploiting multi-modal features, e.g., text, network, and time. (2) A multi-task learning network that simultaneously fits the time intervals and estimates the bullying likelihood based on a Gaussian Mixture Model. The proposed model jointly optimizes the parameters of both components to overcome the shortcomings of decoupled training. Our core contribution is an unsupervised cyberbullying detection model that not only experimentally outperforms the state-of-the-art unsupervised models, but also achieves competitive performance compared to supervised models.
more »
« less
XBully: Cyberbullying Detection within a Multi-Modal Context
Over the last decade, research has revealed the high prevalence of cyberbullying among youth and raised serious concerns in society. Information on the social media platforms where cyberbullying is most prevalent (e.g., Instagram, Facebook, Twitter) is inherently multi-modal, yet most existing work on cyberbullying identification has focused solely on building generic classification models that rely exclusively on text analysis of online social media sessions (e.g., posts). Despite their empirical success, these efforts ignore the multi-modal information manifested in social media data (e.g., image, video, user profile, time, and location), and thus fail to offer a comprehensive understanding of cyberbullying. Conventionally, when information from different modalities is presented together, it often reveals complementary insights about the application domain and facilitates better learning performance. In this paper, we study the novel problem of cyberbullying detection within a multi-modal context by exploiting social media data in a collaborative way. This task, however, is challenging due to the complex combination of both cross-modal correlations among various modalities and structural dependencies between different social media sessions, and the diverse attribute information of different modalities. To address these challenges, we propose XBully, a novel cyberbullying detection framework, that first reformulates multi-modal social media data as a heterogeneous network and then aims to learn node embedding representations upon it. Extensive experimental evaluations on real-world multi-modal social media datasets show that the XBully framework is superior to the state-of-the-art cyberbullying detection models.
more »
« less
- Award ID(s):
- 1719722
- NSF-PAR ID:
- 10110260
- Date Published:
- Journal Name:
- The 12th ACM International Conference on Web Search and Data Mining (WSDM)
- Page Range / eLocation ID:
- 339 - 347
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Cyberbullying is rapidly becoming one of the most serious online risks for adolescents. This has motivated work on machine learning methods to automate the process of cyberbullying detection, which have so far mostly viewed cyberbullying as one-off incidents that occur at a single point in time. Comparatively less is known about how cyberbullying behavior occurs and evolves over time. This oversight highlights a crucial open challenge for cyberbullying-related research, given that cyberbullying is typically defined as intentional acts of aggression via electronic communication that occur repeatedly and persistently . In this article, we center our discussion on the challenge of modeling temporal patterns of cyberbullying behavior. Specifically, we investigate how temporal information within a social media session, which has an inherently hierarchical structure (e.g., words form a comment and comments form a session), can be leveraged to facilitate cyberbullying detection. Recent findings from interdisciplinary research suggest that the temporal characteristics of bullying sessions differ from those of non-bullying sessions and that the temporal information from users’ comments can improve cyberbullying detection. The proposed framework consists of three distinctive features: (1) a hierarchical structure that reflects how a social media session is formed in a bottom-up manner; (2) attention mechanisms applied at the word- and comment-level to differentiate the contributions of words and comments to the representation of a social media session; and (3) the incorporation of temporal features in modeling cyberbullying behavior at the comment-level. Quantitative and qualitative evaluations are conducted on a real-world dataset collected from Instagram, the social networking site with the highest percentage of users reporting cyberbullying experiences. Results from empirical evaluations show the significance of the proposed methods, which are tailored to capture temporal patterns of cyberbullying detection.more » « less
-
Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though quite a few rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent semantics between images and texts, and rarely spot the inconsistency among the post contents and background knowledge. In addition, they commonly assume the completeness of multiple modalities and thus are incapable of handling handle missing modalities in real-life scenarios. Motivated by the intuition that rumors in social media are more likely to have inconsistent semantics, a novel Knowledge-guided Dual-consistency Network is proposed to detect rumors with multimedia contents. It uses two consistency detection subnetworks to capture the inconsistency at the cross-modal level and the content-knowledge level simultaneously. It also enables robust multi-modal representation learning under different missing visual modality conditions, using a special token to discriminate between posts with visual modality and posts without visual modality. Extensive experiments on three public real-world multimedia datasets demonstrate that our framework can outperform the state-of-the-art baselines under both complete and incomplete modality conditions.more » « less
-
Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though quite a few rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent semantics between images and texts, and rarely spot the inconsistency among the post contents and background knowledge. In addition, they commonly assume the completeness of multiple modalities and thus are incapable of handling handle missing modalities in real-life scenarios. Motivated by the intuition that rumors in social media are more likely to have inconsistent semantics, a novel Knowledge-guided Dual-consistency Network is proposed to detect rumors with multimedia contents. It uses two consistency detection subnetworks to capture the inconsistency at the cross-modal level and the content-knowledge level simultaneously. It also enables robust multi-modal representation learning under different missing visual modality conditions, using a special token to discriminate between posts with visual modality and posts without visual modality. Extensive experiments on three public real-world multimedia datasets demonstrate that our framework can outperform the state- of-the-art baselines under both complete and incomplete modality conditions.more » « less
-
Cyberbullying has become one of the most pressing online risks for adolescents and has raised serious concerns in society. Traditional efforts are primarily devoted to building a single generic classification model for all users to differentiate bullying behaviors from the normal content [6, 3, 1, 2, 4]. Despite its empirical success, these models treat users equally and inevitably ignore the idiosyncrasies of users. Recent studies from psychology and sociology suggest that the occurrence of cyberbullying has a strong connection with the personality of victims and bullies embedded in the user-generated content, and the peer influence from like-minded users. In this paper, we propose a personalized cyberbullying detection framework PI-Bully with peer influence in a collaborative environment to tailor the prediction for each individual. In particular, the personalized classifier of each individual consists of three components: a global model that captures the commonality shared by all users, a personalized model that expresses the idiosyncratic personality of each specific user, and a third component that encodes the peer influence received from like-minded users. Most of the existing methods adopt a two-stage approach: they first apply feature engineering to capture the cyberbullying patterns and then employ machine learning classifiers to detect cyberbullying behaviors. However, building a personalized cyberbullying detection framework that is customized to each individual remains a challenging task, in large part because: (1) Social media data is often sparse, noisy and high-dimensional (2) It is important to capture the commonality shared by all users as well as idiosyncratic aspects of the personality of each individual for automatic cyberbullying detection; (3) In reality, a potential victim of cyberbullying is often influenced by peers and the influences from different users could be quite diverse. Hence, it is imperative to develop a way to encode the diversity of peer influence for cyberbullying detection. To summarize, we study a novel problem of personalized cyberbullying detection with peer influence in a collaborative environment, which is able to jointly model users' common features, unique personalities and peer influence to identify cyberbullying cases.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3289600.3291037
