Detecting Event-synced Navigation Attacks across User-generated Content Platforms

Consumers use brand-related user-generated content (UGC), such as online consumer reviews, for their pre-purchase information seeking. However, previous research on consumer information seeking has scarcely explored how purchase situations and product type influence one’s use of brand-related UGC. The purpose of this study is to shed light on this area of consumer behavior. By utilizing linear mixed effect models, we examined several hypotheses relevant to relationships between four purchase variables (PDI, think purchase, feel purchase, online/offline purchase) and one’s reliance on brand-related UGC. The results indicate that all of the purchase variables are positively associated with the use of UGC. Furthermore, the study found several more associations between demographic factors and the reliance on UGC. Age, gender, marital status, number of children in a household, and employment status showed significant associations, whereas education, household income, and ethnicity did not. Implications, limitations and suggestions for future research are also noted.

Using an evaluation framework, this study sheds light on the organizational factors that influence the use of user-generated content (UGC) in radio newsrooms. Although communication research is consistently emphasizing rapid changes in technology and the need for journalists to adapt to their multi-platform environments, this study shows that some radio journalists and managers, at least, are still quite conservative in their innovative practices. Through the use of semi-structured interviews with radio managers and journalists in a major Canadian market, five main uses of UGC were identified in the newsroom. These factors were directly related to the perceived notion of the audience and its value to news reporting. Financial pressures, company policy and professional standards are the three main organizational factors that influence these uses and the extent to which the practices were observed. Age, language and sectorial differences did not appear to be influential in this study. These results, however, suggest that work still needs to be done to establish which factors are most relevant in overcoming fears and challenges with respect to innovative UGC use. A better knowledge of the audience could also influence future behaviour.

The aim of the artice. The article aims to investigate the impact of user-generated content (UGC) on social media marketing, analyze its effectiveness across different platforms, systematize approaches to utilizing UGC, and develop recommendations for optimizing marketing strategies to enhance audience engagement and brand recognition. Analysis results. The analysis results show that user-generated content (UGC) and influencer marketing are key elements of modern brand promotion strategies. UGC stands out for its authenticity and trustworthiness, as it is created by ordinary users, while influencer marketing allows brands to control content and leverage the influence of well-known individuals. Instagram remains the primary platform for UGC, but TikTok is gaining popularity due to its dynamic content. Both platforms effectively engage younger audiences, while YouTube and Facebook have their specific audiences and approaches to utilizing UGC. The analysis of brand collaborations with content creators highlights that significant reach can be achieved through high-quality and engaging content. Special attention should be paid to video content in the format of Reels and TikTok, which ensures high audience interaction. Conclusions and prospects for further research. It has been confirmed that user-generated content (UGC) is a powerful tool in modern marketing, fundamentally changing the role of audience engagement and increasing brand trust. It has been found that different social media platforms have their unique advantages and specific impacts on marketing outcomes, emphasizing the importance of adapting UGC to the characteristics of each platform. Instagram and TikTok are optimal for creating visual content, while YouTube and Facebook are better suited for in-depth exploration of topics through detailed videos and texts. Recommendations have been developed to enhance the effectiveness of marketing strategies through the use of UGC, and approaches to its application across various social media platforms have been systematized. It is suggested that UGC increases conversion rates, as consumers tend to trust content created by other users more than traditional advertising materials. Brands are advised to actively integrate UGC into their marketing strategies to enhance security effectiveness and ensure sustainable growth in the digital market environment. Further research could focus on a detailed examination of the impact of different UGC formats on specific audience segments and develop optimal approaches for its use across various platforms.

Social engineering attacks are considered one of the most hazardous cyberattacks in cybersecurity, as human vulnerabilities are often the weakest link in the entire network. Such vulnerabilities are becoming increasingly susceptible to network security risks. Addressing the social engineering attack defense problem has been the focus of many studies. However, two main challenges hinder its successful resolution. Firstly, the vulnerabilities in social engineering attacks are unique due to multistage attacks, leading to incorrect social engineering defense strategies. Secondly, social engineering attacks are real-time, and the defense strategy algorithms based on gaming or reinforcement learning are too complex to make rapid decisions. This paper proposes a multiattribute quantitative incentive method based on human vulnerability and an improved Q-learning (IQL) reinforcement learning method on human vulnerability attributes. The proposed algorithm aims to address the two main challenges in social engineering attack defense by using a multiattribute incentive method based on human vulnerability to determine the optimal defense strategy. Furthermore, the IQL reinforcement learning method facilitates rapid decision-making during real-time attacks. The experimental results demonstrate that the proposed algorithm outperforms the traditional Q-learning (QL) and deep Q-network (DQN) approaches in terms of time efficiency, taking 9.1% and 19.4% less time, respectively. Moreover, the proposed algorithm effectively addresses the non-uniformity of vulnerabilities in social engineering attacks and provides a reliable defense strategy based on human vulnerability attributes. This study contributes to advancing social engineering attack defense by introducing an effective and efficient method for addressing the vulnerabilities of human factors in the cybersecurity domain.

UGC involvement, motivation and personality: Comparison between China and Spain

Pervasive games integrate physical and social aspects of the real world into a unified gaming experience, where virtual and physical game elements co-exist. The blending of game content with the context of the physical environment has been recognized as an important element that increases the enjoyment, interest and engagement of players in in this game genre. To address this requirement most pervasive games prototypes so far have been bound to specific locations, being heavily dependent on orchestration teams to edit game content and support the overall game execution. However, the capacity of pervasive games to be staged anywhere (i.e. their portability) is critical for their wider adoption and commercial success. Since the manual editing of game content by game developers is clearly incompatible with portability, User-Generated Content (UGC) remains as the only practical option for content provision in pervasive games. Nevertheless, the impact of UGC has not been investigated so far in the pervasive games literature. This article introduces Barbarossa, a pervasive role-playing game incorporating UGC as a means for game content generation. The user trials of Barbarossa confirmed that the use of UGC can enhance the quality of experience perceived by players. We also document good practices with respect to the effective incorporation of UGC in pervasive games, which could be useful to future developers.

PurposeThe purpose of this paper is to use user-generated content (UGC) on social media platforms to infer the possible place brand identities of two famous metropolitan areas in Bangkok, Thailand, namely, Khaosan Road and Yaowarat (Bangkok’s Chinatown), both of which are famous for their street vendors and nightlife. These two places are interesting study sites because of recent identity conflicts among their stakeholders. The method developed in this research can help other places to better understand place brand identities and, as such, effectively plan for and manage those places.Design/methodology/approachThe author used content analysis to study 782 user-generated images on Flickr and 9,633 user-generated textual reviews of Khaosan Road and Yaowarat from TripAdvisor and Google Maps’ Local Guide. MAXQDA was used to code all the images. User-generated textual reviews were studied using Leximancer. The author also introduced a positivity of concept analysis to identify positive and negative components of place brand identity.FindingsThe author developed a place brand identity framework that includes three pillars, namely, place physics, place practices and place personality. Content analysis of the images generated 105 codes and a count of the frequency of the codes that represent place brand identity. Content analysis of textual reviews created the concepts in the three pillars and identified the positive and negative concepts for both places. The results of both image and text analyses showed that street food vending is one of the most salient components of place brand identity for both Khaosan Road and Yaowarat.Practical implicationsThe author suggested several place branding strategies for the Bangkok Metropolitan Administration such as turning Khaosan Road into a music scene for both visitors and locals, controlling excessive and aggressive commercialism, sponsoring the production of creative and authentic content, initiating a compelling online campaign that focusses on the items sold in Yaowarat, hosting a spotlight event such as a seafood festival and improving hygiene and walkability.Originality/valueBoth the advancement of digital technologies and the complexity of stakeholders create a need for empirical studies on place branding involving the participation of the widest possible range of stakeholders and studies on the influence of social media. This research is the first to use both image and text analyses to study place brand identity from UGC. The use of both analyses allows the two methods to complement one another while mitigating the weaknesses of each.

In information security context, social engineering is defined as malicious activities caused by cybercriminals by means of human interactions. It is mainly a psychological manipulation technique which gets benefit of human error to reach private information. This study used machine learning algorithms to predict individuals’ susceptibility to be tricked by social engineering attacks. Simulated scenarios were presented to study participants, and they were asked to identify whether each scenario was a social engineering attack or not. Different kinds of attacks related to various industries were integrated to social engineering simulations. For each participant, different types of social engineering scores were calculated according to their responses. Besides simulations, questionnaires related to demographics, technology usage, and personality traits were filled out by the participants. All of these collected data were used in building predictive classification and regression machine learning models. Through regression and classification models, it was aimed to proactively predict individuals’ social engineering risk levels and classify them into different risk groups in terms of different attack types. This research revealed that it is possible to predetermine the social engineering risk levels of individuals. This important finding means that possible attacks can be prevented by raising awareness before the attack occurs. Within the scope of this study, a social engineering risk detection mobile application has also been developed to give practitioners and policy makers an idea of what kind of systems can be developed in order to determine the risk levels of individuals and then to educate them about various attacks. The ones who need to take action against social engineering attacks will get benefit from findings of this research.

The substantial growth of Internet-connected devices within the Internet of Things (IoT) has given rise to significant security challenges. Among the various threats confronting these interconnected devices, Denial of Service (DoS)/Distributed Denial of Service (DDoS) attacks emerge as significant concerns. The attacks, which seek to disrupt IoT services by flooding networks with unnecessary traffic, there is a critical need for robust security measures. Intrusion Detection Systems (IDS) are vital in identifying suspicious activities, yet many existing systems lack real-time capabilities to address evolving attack strategies. This study investigates the vulnerabilities of IoT networks and the pressing need to detect and classify DoS/DDoS attacks in real time. Traditional IDS, while effective in recognizing known attack patterns, fall short in identifying new attack types due to their reliance on historical data. To bridge this gap, this research focuses on developing an enhanced hybrid network intrusion detection and classification system. This study seeks to make contribution to the advancement of resilient security measures within IoT environments. The framework uses the Kth Nearest Neighbor (KNN) algorithm and dense neural networks to efficiently detect and categorize DoS/DDoS attacks in real time. To achieve this, a simulation model implementing the proposed hybrid algorithm will be developed. The framework utilized the MQTT-IoT-IDS2020 dataset, and a comparison was presented with recent approaches found in the literature. The results demonstrated enhanced performance in terms of true positive rate, accuracy, speed of detection.

Due to their openness and low publishing barrier nature, User-Generated Content (UGC) platforms facilitate the creation of huge amounts of inaccurate content. Consequently, assessing UGC information credibility is developing into a vitally important research topic. This paper offers a systematic review of the current developments in assessing information credibility automatically in UGC platforms, focusing on microblogging service. It covers different aspects from dataset collection and feature usage, through classification techniques, to performance evaluation. A novel theoretical credibility model which integrates the evaluators' traits and context factors to assess information credibility is also presented along with important directions for future research on UGC information credibility.

Consumers visiting platforms that host user-generated content (UGC) not only consume content but also generate content by investing time and effort. This paper seeks to examine a UGC platform’s content provision strategy: how a UGC platform can motivate consumers to generate UGC and how it can manage the balance between UGC and the platform’s own content. As UGC and the platform’s own content perform the same function, one may be inclined to think that the two types of content are substitutes. Our analysis shows that they could function as strategic complements. This is because increasing the platform’s own content provision raises the quality of content on the platform, motivates more consumers to join the platform, and increases the total UGC provision on the platform. The public good characteristics of UGC could prompt us to think that UGC provision on the platform will be less than the socially optimal level. Our analysis identifies conditions when the total provision of UGC can be more than the social optimum. One may wonder whether it is profitable for a UGC platform to completely dispense with its own content. We find that it is always profitable for the UGC platform to offer some of its own content. This is because when consumers spend more time consuming the content, the platform can monetize their attention and earn higher ad revenue. Additionally, we find that an increase in ad space on the platform may motivate greater provision of UGC. Finally, we extend the model in several different directions and find that our results are robust. This paper was accepted by Raphael Thomadsen, marketing. Funding: W. Shin gratefully acknowledges financial support from the Brian R. Gamache Endowed Professorship at the University of Florida. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.01066 .

Nowadays, user-generated content (UGC) has become an important part of Internet user data. This study aims to develop an innovative user identification approach based on UGC platforms. To achieve the objective, this research proposed i) a web mining process to crawl UGC data; ii) a lead user identification index system for evaluating the innovation capability of users; and iii) a user classification process based on K-means clustering according to their UGC performance. Particularly, the complete user performance data of more than 100 users on Douban (one of the biggest UGC platforms in China) were collected, and the web mining, factor analysis, and clustering algorithm was integrated to process the data and classify user groups according to their UGC performance. The classification results were verified through incorporating expertise, and it showed that the classification can exactly recognize the users with proper lead userness. This research is expected to help small and medium enterprises without powerful big data ability to identify innovative users and valuable UGC data more efficiently and facilitate the further product improvement.

The term "Cyber Security" is getting more and more popular and important over the last few years. Since computers and most of the devices are connected to the internet, they are likely to be hacked by the hackers. In the past, this issue was not a big problem, because not every device is required to be connected to the internet or the internet was not popular as today. But the case has changed over the years. All the changes in the technology area also changed the cyber-attack models as well. With the development of technology and the change of usage of the internet over the last years cyber-attacks have become more common and popular by the hackers. Hackers have discovered that they have an opportunity to steal or earn money in a short time without having to spend too much effort. These days, the type of cyber-attacks is not the same as the ones in the past. As time passes, the cyber-attack methods are also changing and evolving. Today, hackers are using more advanced and effective cyber-attack methods compared to past years. There are many methods that are impossible to cover all of them in this article. Our main focus will be on social engineering attacks in this article. Social engineering attacks use different approaches to cyber attacking. Unlike trying to explode a victim’s social media password etc. using advanced exporting programs, algorithms or techniques, social engineering attacks focus on fooling victims into providing their data to hackers by themselves without using or implementing any password cracking, exploiting techniques etc. We will go over what social media engineering is, type of social engineering methods, what countermeasures can be used to protect from social engineering and more in this article.

The article examines the use of user-generated content (UGC) by regional media, its impact on audiences, and the specific features of media–blogger collaboration. The author explores, from a regional perspective, how local media outlets in Karakalpakstan utilize various communication channels and identifies common mistakes in their practices. In particular, the study analyzes the classification of existing communication channels in the region, their potential, and the aspects prioritized when establishing connections with audiences. The methods and forms of collaboration employed by these channels to engage audiences and cover local events are thoroughly examined through both local and international case studies. Furthermore, the article investigates the influence of user-generated content on the regional media agenda and highlights its growing role as an integral component of the media flow.

In defense-in-depth, humans have always been the weakest link in cybersecurity. However, unlike common threats, social engineering poses vulnerabilities not directly quantifiable in penetration testing. Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware. Social Engineering (SE) in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic. In this paper, a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory (RNN-LSTM) to identify well-disguised SE threats in social media posts. We use a custom dataset crawled from hundreds of corporate and personal Facebook posts. First, the social engineering attack detection pipeline (SEAD) is designed to filter out social posts with malicious intents using domain heuristics. Next, each social media post is tokenized into sentences and then analyzed with a sentiment analyzer before being labelled as an anomaly or normal training data. Then, we train an RNN-LSTM model to detect five types of social engineering attacks that potentially contain signs of information gathering. The experimental result showed that the Social Engineering Attack (SEA) model achieves 0.84 in classification precision and 0.81 in recall compared to the ground truth labeled by network experts. The experimental results showed that the semantics and linguistics similarities are an effective indicator for early detection of SEA.