The rapid expansion of smart technologies and IoT has made Wireless Sensor Networks (WSNs) essential for real-time applications such as industrial automation, environmental monitoring, and healthcare. Despite advances in sensor node technology, energy efficiency remains a key challenge due to the limited battery life of nodes, which often operate in remote environments. Effective clustering, where Cluster Heads (CHs) manage data aggregation and transmission, is crucial for optimizing energy use. Motivated from the above, in this paper, we introduce a novel metaheuristic approach called Hippopotamus Optimization-Based Cluster Head Selection (HO-CHS), designed to enhance CH selection by dynamically considering factors such as residual energy, node location, and network topology. Inspired by natural behaviors, HO-CHS effectively balances energy loads, reduces communication distances, and boosts network scalability and reliability. The proposed scheme achieves a 35% increase in network lifetime and a 40% improvement in stability period in comparison to the other existing schemes in literature. Simulation results demonstrate that HO-CHS significantly reduces energy consumption and enhances data transmission efficiency, making it ideal for IoT-enabled consumer electronics networks requiring consistent performance and energy conservation.

Online social media (OSM) communications sometimes turn into hate-filled and offensive comments or arguments. It not just disrupts the social fabric online, but also leads to hate, violence, and crime, in the real physical world in worst scenarios. The existing content moderation practices of OSM platforms often fail to control the online hate. In this article, we develop a deep learning model calledBiCapsHateto detect hate speech (HS) in OSM posts. The model consists of five layers of deep neural networks. It starts with an input layer to process the input text and follows on to an embedding layer to embed the text into a numeric representation. A BiCaps layer then learns the sequential and linguistic contextual representations, a dense layer prepares the model for final classification, and lastly the output layer produces the resulting class as either hate or non-HS (NHS). The BiCaps layer, being the most important component, effectively learns the contextual information with respect to different orientations in both forward and backward directions of the input text via capsule networks. It is further aided by our rich set of hand-crafted shallow and deep auxiliary features including theHatebaselexicon, making the model well-informed. We conduct extensive experiments on five benchmark datasets to demonstrate the efficacy of the proposedBiCapsHatemodel. In the overall results, we outperform the existing state-of-the-art methods including fBERT, HateBERT, and ToxicBERT.BiCapsHateachieves up to 94% and 92% f-score on balanced and imbalanced datasets, respectively. Our complete source code is publicly available at GitHub repositoryhttps://github.com/Ashraf-Kamal/BiCapsHate.

In this paper, we present a multi-task learning framework consisting of two interrelated components for the joint modeling of stance classification and rumor veracity prediction on Twitter. The proposed hierarchical framework models a conversation sequence using a graph attention network, leveraging a BERT-based word representation augmented with user credibility information. The lower component of the framework models the conversation sequence of a claim in addition to the BERT-based user content representation to predict the stance of the underlying tweets. It employs a modified graph attention network, which models a conversation thread by finding tweets path-to-root conversation sequences. Further, the learned stance representation is augmented with the users' credibility information and content representation to predict rumor veracity. The experimental evaluation results over two benchmark datasets show that the proposed approach outperforms the state-of-the-art methods.

Domain-specific keyword extraction is a vital task in the field of text mining. There are various research tasks, such as spam e-mail classification, abusive language detection, sentiment analysis, and emotion mining, where a set of domain-specific keywords (aka lexicon) is highly effective. Existing works for keyword extraction list all keywords rather than domain-specific keywords from a document corpus. Moreover, most of the existing approaches perform well on formal document corpuses but fail on noisy and informal user-generated content in online social media. In this article, we present a hybrid approach by jointly modeling the local and global contextual semantics of words, utilizing the strength of distributional word representation and contrasting-domain corpus for domain-specific keyword extraction. Starting with a seed set of a few domain-specific keywords, we model the text corpus as a weighted word-graph. In this graph, the initial weight of a node (word) represents its semantic association with the target domain calculated as a linear combination of three semantic association metrics, and the weight of an edge connecting a pair of nodes represents the co-occurrence count of the respective words. Thereafter, a modified PageRank method is applied to the word-graph to identify the most relevant words for expanding the initial set of domain-specific keywords. We evaluate our method over both formal and informal text corpuses (comprising six datasets), and show that it performs significantly better in comparison to state-of-the-art methods. Furthermore, we generalize our approach to handle the language-agnostic case, and show that it outperforms existing language-agnostic approaches.

Online Social Networks (OSNs) are witnessing sophisticated cyber threats, that are generally conducted using fake or compromised profiles. Automated agents (aka socialbots), a category of sophisticated and modern threat entities, are the native of the social media platforms and responsible for various modern weaponized information-related attacks, such as astroturfing, misinformation diffusion, and spamming. Detecting socialbots is a challenging and vital task due to their deceiving character of imitating human behavior. To this end, this paper presents an attention-aware deep neural network model, DeepSBD, for detecting socialbots on OSNs. The DeepSBD models users' behavior using profile, temporal, activity, and content information. It jointly models OSN users' behavior using Bidirectional Long Short Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures. It models profile, temporal, and activity information as sequences, which are fed to a two-layers stacked BiLSTM, whereas content information is fed to a deep CNN. We have evaluated DeepSBD over five real-world benchmark datasets and found that it performs significantly better in comparison to the state-of-the-arts and baseline methods. We have also analyzed the efficacy of DeepSBD at different ratios of socialbots and benign users and found that an imbalanced dataset moderately affects the classification accuracy. Finally, we have analyzed the discrimination power of different behavioral components, and it is found that both profile characteristics and content behavior are most impactful, whereas diurnal temporal behavior is the least effective for detecting socialbots on OSNs.

In this paper, we present a graph-theoretic embedding-based approach to model user-generated contents in online social media for rumor detection. Starting with a small set of seed rumor words of four different lexical categories, we generate a words co-occurrence graph and apply centrality-based analysis to identify prominent rumor characterizing words. Thereafter, word embedding is applied to represent each category of seed words as numeric vectors and to train three different classification models for rumor detection. The performance of the proposed approach is empirically evaluated over two versions of a benchmark dataset. The proposed approach is also compared with one of the state-of-the-art methods for rumor detection and performs significantly better.

Twitter, a microblogging online social network, is one of the most popular information sharing and communication platform. The large user-base and users mutual interactions generate massive amount of data that are rich source of information for predictive modeling, sentiment analysis, opinion mining, and other text information processing tasks. Understanding context embedded within text corpus and generating a contextual summary of the corpus is one of the promising research directions in the field of data analytics. In this paper, we present a layered graph-based approach using both content and structural data to analyze and summarize tweets at different levels of granularity. The proposed approach models tweets as a multi-dimensional graph and applies random walk to identify most informative tweets, which are further processed using a graph-theoretic approach, LexRank, to identify most informative sentences for summary generation. Finally, the summary texts are analyzed using TextRank algorithm to identify prominent keywords conceptualizing the context of the underlying corpus. The proposed summary generation and context learning approach is evaluated over four different real-world Twitter datasets using standard information retrieval metrics.

Twitter is one of the most popular microblogging services, which is generally used to share news and updates through short messages restricted to 280 characters. However, its open nature and large user base are frequently exploited by automated spammers, content polluters, and other ill-intended users to commit various cybercrimes, such as cyberbullying, trolling, rumor dissemination, and stalking. Accordingly, a number of approaches have been proposed by researchers to address these problems. However, most of these approaches are based on user characterization and completely disregarding mutual interactions. In this paper, we present a hybrid approach for detecting automated spammers by amalgamating community-based features with other feature categories, namely metadata-, content-, and interaction-based features. The novelty of the proposed approach lies in the characterization of users based on their interactions with their followers given that a user can evade features that are related to his/her own activities, but evading those based on the followers is difficult. Nineteen different features, including six newly defined features and two redefined features, are identified for learning three classifiers, namely, random forest, decision tree, and Bayesian network, on a real dataset that comprises benign users and spammers. The discrimination power of different feature categories is also analyzed, and interaction- and community-based features are determined to be the most effective for spam detection, whereas metadata-based features are proven to be the least effective.

Online social networks are facing serious threats due to presence of human-behaviour imitating malicious bots (aka socialbots) that are successful mainly due to existence of their duped followers. In this paper, we propose an approach to categorize Twitter users into three groups - active, reactive, and inactive targets, based on their interaction behaviour with socialbots. Active users are those who themselves follow socialbots without being followed by them, reactive users respond to the following socialbots by following them back, whereas inactive users do not show any interest against the following requests from anonymous socialbots. The proposed approach is modelled as both binary and ternary classification problem, wherein users' profile is generated using static and dynamic components representing their identical and behavioural aspects. Three different classification techniques viz Naive Bayes, Reduced Error Pruned Decision Tree, and Random Forest are used over a dataset of 749 users collected through live experiment, and a thorough analyses of the identified users categories is presented, wherein it is found that active and reactive users keep on frequently updating their tweets containing advertising related contents. Finally, feature ranking algorithms are used to rank identified features to analyse their discriminative power, and it is found that following rate and follower rate are the most dominating features.