Social Media Forensics Research Articles

Artificial Intelligence in Social Media Forensics: A Comprehensive Survey and Analysis

Social media platforms have completely revolutionized human communication and social interactions. Their positive impacts are simply undeniable. What has also become undeniable is the prevalence of harmful antisocial behaviors on these platforms. Cyberbullying, misinformation, hate speech, radicalization, and extremist propaganda have caused significant harms to society and its most vulnerable populations. Thus, the social media forensics field was born to enable investigators and law enforcement agents to better investigate and prosecute these cybercrimes. This paper surveys the latest research works in the field to explore how artificial intelligence (AI) techniques are being utilized in social media forensics investigations. We examine how natural language processing can be used to identify extremist ideologies, detect online bullying, and analyze deceptive profiles. Additionally, we explore the literature on GNNs and how they are applied in social network modeling for forensic purposes. We conclude by discussing the key challenges in the field and suggest future research directions.

Advanced Algorithmic Approaches for Scam Profile Detection on Instagram

Social media platforms like Instagram have become a haven for online scams, employing various deceptive tactics to exploit unsuspecting users. This paper investigates advanced algorithmic approaches to combat this growing threat. We explore various machine learning models for scam profile detection on Instagram. Our methodology involves collecting a comprehensive dataset from a trusted source and meticulously preprocessing the data for analysis. We then evaluate the effectiveness of a suite of machine learning algorithms, including decision trees, logistic regression, SVMs, and other ensemble methods. Each model’s performance is measured using established metrics like accuracy, precision, recall, and F1-scores. Our findings indicate that ensemble methods, particularly random forest, XGBoost, and gradient boosting, outperform other models, achieving accuracy of 90%. The insights garnered from this study contribute significantly to the body of knowledge in social media forensics, offering practical implications for the development of automated tools to combat online deception.

Social Media Forensics: An Adaptive Cyberbullying-Related Hate Speech Detection Approach based on Neural Networks with Uncertainty

Social Media Forensics: An Adaptive Cyberbullying-Related Hate Speech Detection Approach based on Neural Networks with Uncertainty

ASMF: Ambient social media forensics chain of custody with an intelligent digital investigation process using federated learning

Ambient Intelligence is a concept that relates to a new paradigm of pervasive computing and has the objective of automating responses from the system to humans without any human intervention. In social media forensics, gathering, analyzing, storing, and validating relevant evidence for investigation in a heterogeneous environment is still questionable. There is no hierarchy for automation, even though standardization and secure processes from data collection to validation have not yet been discussed. This poses serious issues for the current investigation procedures and future evidence chain of custody management. This paper contributes threefold. First, it proposes a framework using a blockchain network with a dual chain of data transmission for privacy protection, such as on-chain and off-chain. Second, a protocol is designed to detect and separate local and global cyber threats and undermine multiple federated principles to personalize search space broadly. Third, this study manages personalized updates by means of optimizing backtracking parameters and automating replacements, which directly affects the reduction of negative influence on the social networking environment in terms of imbalanced and distributed data issues. This proposed framework enhances stability in digital investigation. In addition, the simulation uses an extensive social media dataset in different cyberspaces with a variety of cyber threats to investigate. The proposed work outperformed as compared to traditional single-level personalized search and other state-of-the-art schemes.

Enhancing Digital Forensic Investigation: A Focus on Compact Electronic Devices and Social Media Metadata

The rise of portable electronic devices and social media has led to new criminal activities, necessitating advancements in digital forensics. This paper introduces Small-Scale Digital Device Forensics (SSDDF), focusing on the forensic examination of miniature digital devices often used in crimes. SSDDF addresses the challenges posed by these devices, particularly in extracting and analyzing data from them. A key aspect of this research is exploring ontology in social media forensics, particularly within the Android operating system. This involves extracting digital evidence like user accounts, messages, and images from social media platforms. While the paper primarily focuses on social media data, it acknowledges the importance of the devices used in crimes. The integration of SSDDF and the analysis of Android system structures are highlighted as key advancements in digital forensic methodologies. These enhancements are expected to improve the process of collecting and analyzing digital evidence from both compact electronic devices and social media platforms. The study offers significant contributions to the field of digital forensics. It provides new strategies for more efficient and effective forensic investigations, especially in the context of extracting and analyzing digital evidence from small electronic devices and social media, thus paving the way for more robust digital evidence handling in future forensic inquiries.

Digital Forensic Investigation Tools for Cases Related to Social Media and Cybersecurity

The entire world has moved into the digital realm as a result of the quickening pace of technological development. However, this change has also led to an increase in cybercrimes and security breach occurrences, which endanger user security and privacy. As a result, this study sought to examine how digital forensics, a significant advancement in cybersecurity, is used to combat cybercrime. The most recent developments in digital forensics, such as cloud forensics, social media forensics, and IoT forensics, have been examined in this study. With the use of these technologies, cybersecurity experts can protect data while identifying fraudsters by using the digital footprints that data processing and storage leave behind. Technical, operational, and personnel-related issues are among the specific dangers to digital forensics that have been identified by the research. These systems' high level of complexity, volume of data, chain of custody, personnel integrity, and the validity and accuracy of digital forensics are all significant barriers to their widespread adoption. However, the research has also noted the use of artificial intelligence, intrusion detection, and USB forensics as significant prospects for digital forensics that can make the processes simpler, more effective, and safe.

Social Media Metadata Forensic Ontology Model

The escalating use of compact electronic gadgets like smartphones and smartwatches, along with social media platforms, has led to a new dimension of criminal activities. Advancements in digital forensics, which investigates digital evidence, have become noteworthy. Researchers David Christopher Harrill and Richard P. Mislan devised Small-Scale Digital Device Forensics (SSDDF) for examining miniature digital devices used in criminal undertakings. Edlira Kalemi and Sule Yildirim-Yayilgan demonstrated the application of ontology in social media forensics, deciphering Android system frameworks related to social media data extraction. However, their investigations mainly focused on social media, overlooking the role of gadgets used by criminals and victims. Extracting digital data from these devices remains pivotal for securing relevant evidence from social media. Notwithstanding, incorporating SSDDF and understanding Android system structures have significantly contributed to digital forensic methodologies. These enhancements can bolster the investigation process, capturing and analyzing critical digital evidence from compact electronic appliances and social media platforms. The advent of SSDDF and elucidating Android system structures have offered invaluable inputs to digital forensics, enhancing efficacy in amassing significant evidence from small electronic devices and social media platforms.

Use of social networking sites and applications in identification of missing and unidentified persons: A robust forensic tool

Social media is an invaluable tool when seeking information or evidence in criminal cases or in finding missing persons. Social media encompasses vast amount of information such as videos, photos, texts posted by criminals, witnesses and/or victims itself. Social media forensics helps recuperation of electronic evidence from social networking sites which often plays a crucial role in the conviction of a suspect. However, there barely exist any applications which can aid in identifying a deceased person.For identification of deceased, forensic odontologists search for antemortem data such as dental casts, X-ray images, portrait pictures showing the anterior teeth etc., for comparison with the post-mortem data. Archetypal features such as tooth rotations, diastemas, missing teeth, supernumerary teeth, crowded teeth, recognizable removable/fixed prostheses and other attributes could be present in the missing person’s profile and can be crosschecked during the autopsy of the unidentified corpse. For this reason, smile photos represent one of the most common sources of dental information, especially when no technical antemortem dental data is available. To date, there is only one forensic application for smartphones which helps in storage of antemortem smile photo of individuals. Storing the antemortem data that is easily accessible from the social media network aids as an important forensic tool especially in natural disasters or terrorist attacks where human identification process can be arduous.

Criminal Behavior Identification Using Social Media Forensics

Human needs consist of five levels, which are: physiological needs, safety needs, love needs, esteem needs and self-actualization. All these needs lead to human behavior. If the environment of a person is positive, healthy behavior is developed. However, if the environment of the person is not healthy, it can be reflected in his/her behavior. Machines are intelligent enough to mimic human intelligence by using machine learning and artificial intelligence techniques. In the modern era, people tend to post their everyday life events on social media in the form of comments, pictures, videos, etc. Therefore, social media is a significant way of knowing certain behaviors of people such as abusive, aggressive, frustrated and offensive behaviors. Behavior detection by crawling the social media profile of a person is a crucial and important idea. The challenge of behavior detection can be sorted out by applying social media forensics on social media profiles, which involves NLP and deep learning techniques. This paper is based on the study of state of the art work on behavior detection, and based on the research, a model is proposed for behavior detection. The proposed model outperformed with an F1 score of 87% in the unigram + bigram class, and in the bigram + trigram class, it gave an F1 score of 88% when compared with models applied on state of the art work. This study is a great benefit to cybercrime and cyber-security agencies in shortlisting the profiles containing certain behaviors to prevent crimes in the future.

The Role of Social Media Forensics in Digital Forensics

Social media forensics collects evidence from social media sites such as Facebook, WhatsApp, TikTok, and Snapchat to identify criminals. This paper discusses social media crimes such as hacking, photo morphing, shopping scams, cyberbullying, and link baiting. The paper deliberates the social media forensics techniques such as evidence collection, storing, analyzing, and preserving; the paper discusses the process of forensics examination in social media forensics. The paper examines the social media forensics tools such as WebPreserver, make a Website Hub, Pipl Search, TinEye, and TweetBeaver and discusses the applications of each device. The paper concludes by discussing the future of social media forensics.

Social media forensics applied to assessment of post–critical incident social reaction: The case of the 2017 Manchester Arena terrorist attack

Social media forensics applied to assessment of post–critical incident social reaction: The case of the 2017 Manchester Arena terrorist attack

A Methodology For Large-Scale Identification of Related Accounts in Underground Forums

Underground forums allow users to interact with communities focused on illicit activities. They serve as an entry point for actors interested in deviant and criminal topics. Due to the pseudo-anonymity provided, they have become improvised marketplaces for trading illegal products and services, including those used to conduct cyberattacks. Thus, these forums are an important data source for threat intelligence analysts and law enforcement. The use of multiple accounts is forbidden in most forums since these are mostly used for malicious purposes. Still, this is a common practice. Being able to identify an actor or gang behind multiple accounts allows for proper attribution in online investigations, and also to design intervention mechanisms for illegal activities. Existing solutions for multi-account detection either require ground truth data to conduct supervised classification or use manual approaches. In this work, we propose a methodology for the large-scale identification of related accounts in underground forums. These accounts are similar according to the distinctive content posted, and thus are likely to belong to the same actor or group. The methodology applies to various domains and leverages distinctive artefacts and personal information left online by the users. We provide experimental results on a large dataset comprising more than 1.1M user accounts from 15 different forums. We show how this methodology, combined with existing approaches commonly used in social media forensics, can assist with and improve online investigations.

Writer Identification Using Microblogging Texts for Social Media Forensics

Establishing authorship of online texts is fundamental to combat cybercrimes. Unfortunately, text length is limited on some platforms, making the challenge harder. We aim at identifying the authorship of Twitter messages limited to 140 characters. We evaluate popular stylometric features, widely used in literary analysis, and specific Twitter features like URLs, hashtags, replies or quotes. We use two databases with 93 and 3957 authors, respectively. We test varying sized author sets and varying amounts of training/test texts per author. Performance is further improved by feature combination via automatic selection. With a large number of training Tweets (>500), a good accuracy (Rank-5>80%) is achievable with only a few dozens of test Tweets, even with several thousands of authors. With smaller sample sizes (10-20 training Tweets), the search space can be diminished by 9-15% while keeping a high chance that the correct author is retrieved among the candidates. In such cases, automatic attribution can provide significant time savings to experts in suspect search. For completeness, we report verification results. With few training/test Tweets, the EER is above 20-25%, which is reduced to < 15% if hundreds of training Tweets are available. We also quantify the computational complexity and time permanence of the employed features.

Digital forensics in cyber security - recent trends, threats, and opportunities

The rapid technological advancement has led the entire world to shift towards digital domain. However, this transition has also result in the emergence of cybercrimes and security breach incidents that threatens the privacy and security of the users. Therefore, this paper aimed at examining the use of digital forensics in countering cybercrimes, which has been a critical breakthrough in cybersecurity. The paper has analyzed the most recent trends in digital forensics, which include cloud forensics, social media forensics, and IoT forensics. These technologies are helping the cybersecurity professionals to use the digital traces left by the data storage and processing to keep data safe, while identifying the cybercriminals. However, the research has also observed specific threats to digital forensics, which include technical, operational and personnel-related challenges. The high complexity of these systems, large volume of data, chain of custody, the integrity of personnel, and the validity and accuracy of digital forensics are major threats to its large-scale use. Nevertheless, the paper has also observed the use of USB forensics, intrusion detection and artificial intelligence as major opportunities for digital forensics that can make the processes easier, efficient, and safe.

Social media forensics applied to assessment of post–critical incident social reaction: The case of the 2017 Manchester Arena terrorist attack

Forensic science is constantly evolving and transforming, reflecting the numerous technological innovations of recent decades. There are, however, continuing issues with the use of digital data, such as the difficulty of handling large-scale collections of text data. As one way of dealing with this problem, we used machine-learning techniques, particularly natural language processing and Latent Dirichlet Allocation (LDA) topic modeling, to create an unsupervised text reduction method that was then used to study social reactions in the aftermath of the 2017 Manchester Arena bombing. Our database was a set of millions of messages posted on Twitter in the first 24 h after the attack. The findings show that our method improves on the tools presently used by law enforcement and other agencies to monitor social media, particularly following an event that is likely to create widespread social reaction. For example, it makes it possible to track different types of social reactions over time and to identify subevents that have a significant impact on public perceptions.

Authorship Attribution for Social Media Forensics

The veil of anonymity provided by smartphones with pre-paid SIM cards, public Wi-Fi hotspots, and distributed networks like Tor has drastically complicated the task of identifying users of social media during forensic investigations. In some cases, the text of a single posted message will be the only clue to an author's identity. How can we accurately predict who that author might be when the message may never exceed 140 characters on a service like Twitter? For the past 50 years, linguists, computer scientists, and scholars of the humanities have been jointly developing automated methods to identify authors based on the style of their writing. All authors possess peculiarities of habit that influence the form and content of their written works. These characteristics can often be quantified and measured using machine learning algorithms. In this paper, we provide a comprehensive review of the methods of authorship attribution that can be applied to the problem of social media forensics. Furthermore, we examine emerging supervised learning-based methods that are effective for small sample sizes, and provide step-by-step explanations for several scalable approaches as instructional case studies for newcomers to the field. We argue that there is a significant need in forensics for new authorship attribution algorithms that can exploit context, can process multi-modal data, and are tolerant to incomplete knowledge of the space of all possible authors at training time.

Social Media Forensics for Hate Speech Opinion Mining

Social Media Forensics for Hate Speech Opinion Mining