Social media bot detection with deep learning methods: a systematic review

Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are much needed. Over the past few years, a few review studies contributed to the social media bot detection research by presenting a comprehensive survey on various detection methods including cutting-edge solutions like machine learning (ML)/deep learning (DL) techniques. This paper, to the best of our knowledge, is the first one to only highlight the DL techniques and compare the motivation/effectiveness of these techniques among themselves and over other methods, especially the traditional ML ones. We present here a refined taxonomy of the features used in DL studies and details about the associated pre-processing strategies required to make suitable training data for a DL model. We summarize the gaps addressed by the review papers that mentioned about DL/ML studies to provide future directions in this field. Overall, DL techniques turn out to be computation and time efficient techniques for social bot detection with better or compatible performance as traditional ML techniques

Fake Account Identification Using Machine Learning Approaches Integrated with Adaptive Particle Swarm Optimization

 It is customary for humans, bots, and other automated systems to generate new user accounts by utilizing pilfered or otherwise deceitful personal information. They are employed in deceitful activities such as phishing and identity theft, as well as in spreading damaging rumors. An somebody with malevolent intent may generate a substantial number of counterfeit accounts, ranging from hundreds to thousands, with the aim of disseminating their harmful actions to as many authentic users as possible. Users can get a wealth of knowledge from social networking networks. Malicious individuals are readily encouraged to take use of this vast collection of social media information. These cybercriminals fabricate fictitious identities and disseminate meaningless stuff. An essential aspect of using social media networks is the process of discerning counterfeit profiles. This study presents a machine learning approach to detect fraudulent Instagram profiles. This strategy employed the attribute-selection technique, adaptive particle swarm optimization, and feature-elimination recursion. The results indicate that the suggested adaptive particle swarm optimization method surpasses RFE in terms of accuracy, recall, and F measure

An Assessment of Media Consumers’ Ability to Distinguish the Level of Post-Processing in Journalistic Images

Photojournalists are held to a high degree of ethics because of the importance and impact of their work. To address this, several professional photojournalist organizations and publishers have created guidelines on how to appropriately post-process an image. Today the average media consumer is exposed to a diverse news landscape, and there is a tendency for consumers to trust photojournalistic images as being representative of the truth (Farid, 2006). This research considers to what extent can the average media consumer distinguish between ethically and unethically post-processed images. This study aimed to discover how well people can distinguish between three categories of images when viewing them quickly on their mobile devices. Using a web-based survey, participants were asked to identify various images as either original, enhanced, or manipulated. Original images had post-processing limited to cropping and having the aspect ratio changed. Enhanced images had aesthetic changes and did not attempt or intend to change the content or meaning of the image. Manipulated images either had material added, removed, or significantly changed. Furthermore, the image dataset was annotated to describe broad content characteristics such as people vs. no people and inside vs. outside. A Friedman test with a pairwise comparison with a Bonferroni correction was utilized to determine if there were differences in the percentage correct by semantic categories (People/No People, Indoors/Outdoors) and manipulation sub-categories (Add, Remove, Change). Recruited through social media and word of mouth, 1,919 participants responded to an average of 101 images out of a total of a possible 164, with an average of 1,180 responses per image. Participants were encouraged to provide their first impression. Responses were more likely to label the images as original (53.9%) compared to identifying them as enhanced (30.1%) or manipulated (16.0%). On average, only 36% of the images were correctly identified. Overall, participants’ responses indicated that unless the manipulation was overly apparent or semantically absurd, they believed that the image must be either the original or enhanced

Olfaction-enhanced multimedia: Perspectives and challenges

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2011 Springer VerlagOlfaction—or smell—is one of the last challenges which multimedia and multimodal applications have to conquer. Enhancing such applications with olfactory stimuli has the potential to create a more complex—and richer—user multimedia experience, by heightening the sense of reality and diversifying user interaction modalities. Nonetheless, olfaction-enhanced multimedia still remains a challenging research area. More recently, however, there have been initial signs of olfactory-enhanced applications in multimedia, with olfaction being used towards a variety of goals, including notification alerts, enhancing the sense of reality in immersive applications, and branding, to name but a few. However, as the goal of a multimedia application is to inform and/or entertain users, achieving quality olfaction-enhanced multimedia applications from the users’ perspective is vital to the success and continuity of these applications. Accordingly, in this paper we have focused on investigating the user perceived experience of olfaction-enhanced multimedia applications, with the aim of discovering the quality evaluation factors that are important from a user’s perspective of these applications, and consequently ensure the continued advancement and success of olfaction-enhanced multimedia applications

An Ensemble Classification and Hybrid Feature Selection Approach for Fake News Stance Detection

The developments in Internet and notions of social media have revolutionised representations and disseminations of news. News spreads quickly while costing less in social media. Amidst these quick distributions, dangerous or seductive information like user generated false news also spread equally. on social media. Distinguishing true incidents from false news strips create key challenges. Prior to sending the feature vectors to the classifier, it was suggested in this study effort to use dimensionality reduction approaches to do so. These methods would not significantly affect the result, though. Furthermore, utilising dimensionality reduction techniques significantly reduces the time needed to complete a forecast. This paper presents a hybrid feature selection method to overcome the above mentioned issues. The classifications of fake news are based on ensembles which identify connections between stories and headlines of news items. Initially, data is pre-processed to transform unstructured data into structures for ease of processing. In the second step, unidentified qualities of false news from diverse connections amongst news articles are extracted utilising PCA (Principal Component Analysis). For the feature reduction procedure, the third step uses FPSO (Fuzzy Particle Swarm Optimization) to select features. To efficiently understand how news items are represented and spot bogus news, this study creates ELMs (Ensemble Learning Models). This study obtained a dataset from Kaggle to create the reasoning. In this study, four assessment metrics have been used to evaluate performances of classifying models

Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks

Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to its elaborately fabricated contents, making it difficult to obtain large-scale annotations for fake news data. Due to such data scarcity issues, detecting fake news tends to fail and overfit in the supervised setting. Recently, graph neural networks (GNNs) have been adopted to leverage the richer relational information among both labeled and unlabeled instances. Despite their promising results, they are inherently focused on pairwise relations between news, which can limit the expressive power for capturing fake news that spreads in a group-level. For example, detecting fake news can be more effective when we better understand relations between news pieces shared among susceptible users. To address those issues, we propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Experiments based on two benchmark datasets show that our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.Comment: Accepted in IEEE Big Data 2