Investigating User Information and Social Media Features in Cyberbullying Detection

As society grows increasingly more online with each passing year, the problem of cyberbullying becomes more and more prominent, with such incidents having the capacity to negatively impact mental health in a major way, especially among children and teenagers. The proposed approach builds on our previous work that established multi-modal detection of cyberbullying on Twitter, and restructures the multi-modal approach by incorporating social media features such as time-related features and social network information. As a result, the new models reach a classification accuracy between 94.4% and 94.6%, from the previous accuracy of 93%. The proposed new approach affirms the use of context-based data in addition to more directly-related features when analyzing cyberbullying and other interactions with promising improvements. We believe that this work contributes significantly to the study of cyberbullying detection, which is an imminent problem with growing importance in the post-COVID society. © 2022 IEEE.

Detecting compromised social network accounts using deep learning for behavior and text analyses

Social networks allow people to connect to one another. Over time, these accounts become an essential part of one’s online identity. The account stores various personal data and contains one’s network of acquaintances. Attackers seek to compromise user accounts for various malicious purposes, such as distributing spam, phishing, and much more. Timely detection of compromises becomes crucial for protecting users and social networks. This article proposes a novel system for detecting compromises of a social network account by considering both post behavior and textual content. A deep multi-layer perceptron-based autoencoder is leveraged to consolidate diverse features and extract underlying relationships. Experiments show that the proposed system outperforms previous techniques that considered only behavioral information. The authors believe that this work is well-timed, significant especially in the world that has been largely locked down by the COVID-19 pandemic and thus depends much more on reliable social networks to stay connected. Copyright © 2021, IGI Global.

Sentiment analysis on COVID tweets using COVID-Twitter-BERT with auxiliary sentence approach

Sentiment analysis is a fascinating area as a natural language understanding benchmark to evaluate customers' feedback and needs. Moreover, sentiment analysis can be applied to understand the people's reactions to public events such as the presidential elections and disease pandemics. Recent works in sentiment analysis on COVID-19 present a domain-targeted Bidirectional Encoder Representations from Transformer (BERT) language model, COVID-Twitter BERT (CT-BERT). However, there is little improvement in text classification using a BERT-based language model directly. Therefore, an auxiliary approach using BERT was proposed. This method converts single-sentence classification into pair-sentence classification, which solves the performance issue of BERT in text classification tasks. In this paper, we combine a pre-trained BERT model from COVID-related tweets and the auxiliary-sentence method to achieve better classification performance on COVID tweets sentiment analysis. We show that converting single-sentence classification into pair-sentence classification extends the dataset and obtains higher accuracies and F1 scores. However, we expect a domain-specific language model would perform better than a general language model. In our results, we show that the performance of CT-BERT does not necessarily outperform BERT specifically in understanding sentiments. © 2021 ACM.