Cyberbullying is a social media network issue, a global crisis affecting the victims and society. Automatically identifying cyberbullying on social media has become extremely hard because of the complicated nature and intricate language employed within these platforms. The brevity and informal nature of text often results in ambiguous or unclear expressions, making it challenging to accurately interpret the intended meaning. Identifying cyberbullying becomes even more complex when faced with uncertain or contextually vague content. Presently, numerous approaches are available for cyberbullying detection, However, they continue to grapple with the challenge of distinguishing between various forms of cyberbullying-related hate speech due to its ambiguous and vague nature, and they also fall short in terms of accuracy. This paper proposes a novel approach to fine-grained cyberbullying classification by integrating Neutrosophic Logic within the Multi-Layer Perceptron (MLP) model. The proposed model enhances cyberbullying types by mitigating the challenges posed by the ambiguity and overlapping boundaries between distinct categories of cyberbullying. The incorporation of Neutrosophic Logic aims to address the uncertainty, ambiguity, and indeterminacy within classification decisions, offering a more comprehensive and flexible approach for handling complex classification scenarios. The model, leveraging the one-against-one strategy in MLP classification, captures complex relationships between various types of cyberbullying, due to the overlaps and ambiguous instances within cyberbullying types. The testing phase of this model emphasizes the significance of Neutrosophic Logic, employing class probabilities from multiple one-against-one classifiers to provide a comprehensive insight into classification outcomes. The results of the proposed model demonstrate the performance enhancement of incorporating Neutrosophic Logic for fine-grained cyberbullying classification tasks.

One main incentive for the utilization of opinion mining (OM) in social media is the impossibility of manually analyzing millions of opinions. However, applying OM for decision-making requires imitating human brain reasoning for more pragmatic results. Analyzing opinions in human-like intelligence is vital for avoiding misleading results. This occurs when the process can properly excogitate social influence and environmental uncertainty. In this study, an OM model for Twitter is proposed to handle perspectivism and opinion ambiguity. For perspectivism, social network analysis (SNA) is conducted, where users are ranked and then weighted using the UCINET tool and neural networks. An uncertainty classifier is used to integrate users’ influence levels with the polarity scores of their texts, providing a new polarity score that can reflect the real-world reasoning of opinions. Polarity scores needed for integration is done using the lexicon resource TextBlob. In the proposed model, three uncertainty classifiers are tested: type1 fuzzy logic (T1- FL), type2 fuzzy logic (T2- FL), and neutrosophic logic (NL). A comparative analysis of the methods shows the ability of NL to deal with the uncertainty existing in the data more accurately, proving the benefit of NL in improving the power of OM in social media.

The contemporary speed at which opinions move on social media makes them an undeniable force in the field of opinion mining (OM). This may cause the OM challenge to become more social than technical. This is when the process can determinately represent everyone to the degree they are worth. Nevertheless, considering perspectivism can result in opinion dynamicity. Pondering the existence of opinion dynamicity and uncertainty can provide smart OM on social media. This study proposes a neutrosophic-based OM approach for Twitter that handles perspectivism, its consequences, and indeterminacy. For perspectivism, a social network analysis (SNA) was conducted using popular SNA tools (e.g., Graphistry). An influence weighting of users was performed using an artificial neural network (ANN) based on the SNA provided output and people’s reactions to the OM analyzed texts. The initiative adoption of neutrosophic logic (NL) to integrate users’ influence with their OM scores is to deal with both the opinion dynamicity and indeterminacy. Thus, it provides new uncertainty OM scores that can reflect everyone. The OM scores needed for integration were generated using TextBlob. The results show the ability of NL to improve the OM process and accurately consider the innumerable degrees. This will eventually aid in a better understanding of people’s opinions, helping OM in social media to become a real pillar of many applications, especially business marketing.