Search Results (16)

Online reviews significantly influence consumer behavior and business reputations. Detecting fake reviews is important for maintaining trust and integrity in these platforms. We present an aspect-based sentiment analysis approach, referred to as FakeDetectionGCN, to distinguish genuine feedback from deceptive content. The idea is to analyze sentiments related to specific aspects (features) within reviews. Graph convolutional networks are used to model the complex contextual dependencies in the review texts. Additionally, SenticNet, an external semantic resource, is integrated to enhance the understanding of sentiments in the reviews. This model is capable of identifying both human-generated (genuine) as well as computer-generated (fake) reviews. It has been evaluated on two types of datasets and has shown strong performance across both. Through this work, we contribute to the effective detection of fake reviews and maintaining a trustworthy online review ecosystem. Full article

Spam reviews pose a significant challenge to the integrity of online platforms, misleading consumers and undermining the credibility of genuine feedback. This paper introduces an innovative AI-generated spam review detection framework that leverages Deep Learning algorithms and Natural Language Processing (NLP) techniques to identify and mitigate spam reviews effectively. Our framework utilizes multiple Deep Learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), to capture intricate patterns in textual data. The system processes and analyzes large volumes of review content to detect deceptive patterns by utilizing advanced NLP and text embedding techniques such as One-Hot Encoding, Word2Vec, and Term Frequency-Inverse Document Frequency (TF-IDF). By combining three embedding techniques with four Deep Learning algorithms, a total of twelve exhaustive experiments were conducted to detect AI-generated spam reviews. The experimental results demonstrate that our approach outperforms the traditional machine learning models, offering a robust solution for ensuring the authenticity of online reviews. Among the models evaluated, those employing Word2Vec embeddings, particularly the BiLSTM_Word2Vec model, exhibited the strongest performance. The BiLSTM model with Word2Vec achieved the highest performance, with an exceptional accuracy of 98.46%, a precision of 0.98, a recall of 0.97, and an F1-score of 0.98, reflecting a near-perfect balance between precision and recall. Its high F2-score (0.9810) and F0.5-score (0.9857) further highlight its effectiveness in accurately detecting AI-generated spam while minimizing false positives, making it the most reliable option for this task. Similarly, the Word2Vec-based LSTM model also performed exceptionally well, with an accuracy of 97.58%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. The CNN model with Word2Vec similarly delivered strong results, achieving an accuracy of 97.61%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. This study is unique in its focus on detecting spam reviews specifically generated by AI-based tools rather than solely detecting spam reviews or AI-generated text. This research contributes to the field of spam detection by offering a scalable, efficient, and accurate framework that can be integrated into various online platforms, enhancing user trust and the decision-making processes. Full article

Digital forensic investigations are becoming increasingly crucial in criminal investigations and civil litigations, especially in cases of corporate espionage and intellectual property theft as more communication occurs online via e-mail and social media. Deceptive opinion spam analysis is an emerging field of research that aims to detect and identify fraudulent reviews, comments, and other forms of deceptive online content. In this paper, we explore how the findings from this field may be relevant to forensic investigation, particularly the features that capture stylistic patterns and sentiments, which are psychologically relevant aspects of truthful and deceptive language. To assess these features’ utility, we demonstrate the potential of our proposed approach using the real-world dataset from the Enron Email Corpus. Our findings suggest that deceptive opinion spam analysis may be a valuable tool for forensic investigators and legal professionals looking to identify and analyze deceptive behavior in online communication. By incorporating these techniques into their investigative and legal strategies, professionals can improve the accuracy and reliability of their findings, leading to more effective and just outcomes. Full article

Intentionally deceptive online content represents a significant issue for society. Multiple techniques have been proposed to identify and combat its spread. To understand how to inform individuals most effectively about the potential biases of and other issues with content, this article studies factors that impact the perception of online content. Specifically, it looks at how these factors have similar or different impact depending on the income level, political affiliation and online usage time of Americans. A national survey was conducted that asked respondents about multiple factors that influence their and others’ perception of online content trustworthiness. It also asked what the ideal impact of these factors should be. This data is presented and analyzed herein, conclusions are drawn and their implications, with regard to preventing the spread of deceptive online content, are discussed. Full article

Online content is consumed by most Americans and is a primary source of their news information. It impacts millions’ perception of the world around them. Problematically, individuals who seek to deceive or manipulate the public can use targeted online content to do so and this content is readily consumed and believed by many. The use of labeling as a way to alert consumers of potential deceptive content has been proposed. This paper looks at factors which impact its perceived trustworthiness and, thus, potential use by Americans and analyzes these factors based on age, education level and gender. This analysis shows that, while labeling and all label types enjoy broad support, the level of support and uncertainty about labeling varies by age and education level with different labels outperforming for given age and education levels. Gender, alternately, was not shown to have a tremendous impact on respondents’ perspectives regarding labeling; however, females where shown to support labeling more, on average, but also report more uncertainty. Full article

Deceptive online content represents a potentially severe threat to society. This content has shown to have the capability to manipulate individuals’ beliefs, voting and activities. It is a demonstrably effective way for foreign adversaries to create domestic strife in open societies. It is also, by virtue of the magnitude of content, very difficult to combat. Solutions ranging from censorship to inaction have been proposed. One solution that has been suggested is labeling content to indicate its accuracy or characteristics. This would provide an indication or even warning regarding content that may be deceptive in nature, helping content consumers make informed decisions. If successful, this approach would avoid limitations on content creators’ freedom of speech while also mitigating the problems caused by deceptive content. To determine whether this approach could be effective, this paper presents the results of a national survey aimed at understanding how content labeling impacts online content consumption decision making. To ascertain the impact of potential labeling techniques on different portions of the population, it analyzes labels’ efficacy in terms of income level, political party affiliation and online usage time. This, thus, facilitates determining whether the labeling may be effective and also aids in understating whether its effectiveness may vary by demographic group. Full article

Online content trustworthiness has become a topic of significant interest due to the growth of so-called ‘fake news’ and other deceptive online content. Deceptive content has been responsible for an armed standoff, caused mistrust surrounding elections and reduced the trust in media, generally. Modern society, though, depends on the ability to share information to function. Citizens may be injured if they don’t heed medical, weather and other emergency warnings. Distrust for educational information impedes the transfer of knowledge of innovations and societal growth. To function properly, societal trust in shared in information is critical. This article seeks to understand the problem and possible solutions. It assesses the impact of the characteristics of online articles and their authors, publishers and sponsors on perceived trustworthiness to ascertain how Americans make online article trust decisions. This analysis is conducted with a focus on how the impact of these factors on trustworthiness varies based on individuals’ age, education and gender. Full article

The advancement of the Internet has changed people’s ways of expressing and sharing their views with the world. Moreover, user-generated content has become a primary guide for customer purchasing decisions. Therefore, motivated by commercial interest, some sellers have started manipulating Internet ratings by writing false positive reviews to encourage the sale of their goods and writing false negative reviews to discredit competitors. These reviews are generally referred to as deceptive reviews. Deceptive reviews mislead customers in purchasing goods that are inconsistent with online information and thus obstruct fair competition among businesses. To protect the right of consumers and sellers, an effective method is required to automate the detection of misleading reviews. Previously developed methods of translating text into feature vectors usually fail to interpret polysemous words, which leads to such functions being obstructed. By using dynamic feature vectors, the present study developed several misleading review-detection models for the Chinese language. The developed models were then compared with the standard detection-efficiency models. The deceptive reviews collected from various online forums in Taiwan by previous studies were used to test the models. The results showed that the models proposed in this study can achieve 0.92 in terms of precision, 0.91 in terms of recall, and 0.91 in terms of F1-score. The improvement rate of our proposal is higher than 20%. Accordingly, we prove that our proposal demonstrated improved performance in detecting misleading reviews, and the models based on dynamic feature vectors were capable of more accurately capturing semantic terms than the conventional models based on the static feature vectors, thereby enhancing effectiveness. Full article

Americans are pervasively exposed to social media, news, and online content. Some of this content is designed to be deliberately deceptive and manipulative. However, it is interspersed amongst other content from friends and family, advertising, and legitimate news. Filtering content violates key societal values of freedom of expression and inquiry. Taking no action, though, leaves users at the mercy of individuals and groups who seek to use both single articles and complex patterns of content to manipulate how Americans consume, act, work, and even think. Warning labels, which do not block content but instead aid the user in making informed consumption decisions, have been proposed as a potential solution to this dilemma. Ideally, they would respect the autonomy of users to determine what media they consume while combating intentional deception and manipulation through its identification to the user. This paper considers the perception of Americans regarding the use of warning labels to alert users to potentially deceptive content. It presents the results of a population representative national study and analysis of perceptions in terms of key demographics. Full article

Intentionally deceptive online content seeks to manipulate individuals in their roles as voters, consumers, and participants in society at large. While this problem is pronounced, techniques to combat it may exist. To analyze the problem and potential solutions, we conducted three surveys relating to how news consumption decisions are made and the impact of labels on decision making. This article describes these three surveys and the data that were collected by them. Full article

Fake news is prevalent in society. A variety of methods have been used in an attempt to mitigate the spread of misinformation and fake news ranging from using machine learning to detect fake news to paying fact checkers to manually fact check media to ensure its accuracy. In this paper, three studies were conducted at two universities with different regional demographic characteristics to gain a better understanding of respondents’ perception of online media labeling techniques. The first study deals with what fields should appear on a media label. The second study looks into what types of informative labels respondents would use. The third focuses on blocking type labels. Participants’ perceptions, preferences, and results are analyzed by their demographic characteristics. Full article

This paper considers the use of a post metadata-based approach to identifying intentionally deceptive online content. It presents the use of an inherently explainable artificial intelligence technique, which utilizes machine learning to train an expert system, for this purpose. It considers the role of three factors (textual context, speaker background, and emotion) in fake news detection analysis and evaluates the efficacy of using key factors, but not the inherently subjective processing of post text itself, to identify deceptive online content. This paper presents initial work on a potential deceptive content detection tool and also, through the networks that it presents for this purpose, considers the interrelationships of factors that can be used to determine whether a post is deceptive content or not and their comparative importance. Full article

So-called ‘fake news’—deceptive online content that attempts to manipulate readers—is a growing problem. A tool of intelligence agencies, scammers and marketers alike, it has been blamed for election interference, public confusion and other issues in the United States and beyond. This problem is made particularly pronounced as younger generations choose social media sources over journalistic sources for their information. This paper considers the prospective solution of providing consumers with ‘nutrition facts’-style information for online content. To this end, it reviews prior work in product labeling and considers several possible approaches and the arguments for and against such labels. Based on this analysis, a case is made for the need for a nutrition facts-based labeling scheme for online content. Full article

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset. Full article

Niche dating sites have become a popular trend in the online dating industry; yet, little is known about the specialization strategies these sites use to cater to their users’ needs. Moreover, previous research alludes to the idea that many of these sites may be engaging in pseudo-individualization—a deceptive technique that creates an illusion of specialization. This study focuses on niche dating sites for older adults, one of the fastest growing niches in online dating. Through a qualitative content analysis and close reading of older-adult dating sites, I seek to determine how and to what extent online dating sites that target older adults actually customize their services to benefit this population. Three key findings emerge: (1) the use of mass segmentation, a strategy that combines elements of both mass marketing and market segmentation; (2) a strategic broadening of the boundaries of the older-adult niche; and (3) the use of deceptive advertising to attract users. These findings suggest that older-adult dating sites are, in fact, engaging in pseudo-individualization. They also highlight some of the unique aspects of online media that facilitate this practice. Implications for both online daters and site producers are discussed. Full article