A survey of uncover misleading and cyberbullying on social media for public health

Misleading health information is a critical phenomenon in our modern life due to advance in technology. In fact, social media facilitated the dissemination of information, and as a result, misinformation spread rapidly, cheaply, and successfully. Fake health information can have a significant effect on human behavior and attitudes. This survey presents the current works developed for misleading information detection (MLID) in health fields based on machine learning and deep learning techniques and introduces a detailed discussion of the main phases of the generic adopted approach for MLID. In addition, we highlight the benchmarking datasets and the most used metrics to evaluate the performance of MLID algorithms are discussed and finally, a deep investigation of the limitations and drawbacks of the current progressing technologies in various research directions is provided to help the researchers to use the most proper methods in this emerging task of MLID.

How many cyberbullying(s)? A non-unitary perspective for offensive online behaviours

Research has usually considered cyberbullying as a unitary phenomenon. Thus, it has been neglected to explore whether the specific online aggressive behaviours relate differentially to demographic features of the perpetrators of online aggressive actions, their personality characteristics, or to the ways in which they interact with the Internet. To bridge this gap, a study was conducted through a questionnaire administered online to 1228 Italian high-school students (Female: 61.1%; 14–15 yo: 48.%; 16–17 yo: 29.1%; 18–20 yo: 20.4%, 21–25 yo: 1.6%; Northern Italy: 4.1%; Central Italy: 59.2%; Southern Italy: 36.4%). The questionnaire, in addition to items about the use of social media, mechanisms of Moral Disengagement and personality characteristics of the participants in the study, also included a scale for the measurement of cyberbullying through the reference to six aggressive behaviours. The results indicate that cyberbullying can be considered as a non-unitary phenomenon in which the different aggressive behaviours can be related to different individual characteristics such as gender, personality traits and the different ways of interacting with social media. Moreover, the existence of two components of cyberbullying has been highlighted, one related to virtual offensive actions directly aimed at a victim, the other to indirect actions, more likely conducted involving bystanders. These findings open important perspectives for understanding, preventing, and mitigating cyberbullying among adolescents.

Multi-class WHMBoost: An ensemble algorithm for multi-class imbalanced data

The imbalanced data problem is widespread in the real world. In the process of training machine learning models, ignoring imbalanced data problems will cause the performance of the model to deteriorate. At present, researchers have proposed many methods to deal with the imbalanced data problems, but these methods mainly focus on the imbalanced data problems in two-class classification tasks. Learning from multi-class imbalanced data sets is still an open problem. In this paper, an ensemble method for classifying multi-class imbalanced data sets is put forward, called multi-class WHMBoost. It is an extension of WHMBoost that we proposed earlier. We do not use the algorithm used in WHMBoost to process the data, but use random balance based on average size so as to balance the data distribution. The weak classifiers we use in the boosting algorithm are support vector machine and decision tree classifier. In the process of training the model, they participate in training with given weights in order to complement each other’s advantages. On 18 multi-class imbalanced data sets, we compared the performance of multi-class WHMBoost with state of the art ensemble algorithms using MAUC, MG-mean and MMCC as evaluation criteria. The results demonstrate that it has obvious advantages compared with state of the art ensemble algorithms and can effectively deal with multi-class imbalanced data sets.