Blog Mining Research Articles

Decoding the Threat Landscape : ChatGPT, FraudGPT, and WormGPT in Social Engineering Attacks

In the ever-evolving realm of cybersecurity, the rise of generative AI models like ChatGPT, FraudGPT, and WormGPT has introduced both innovative solutions and unprecedented challenges. This research delves into the multifaceted applications of generative AI in social engineering attacks, offering insights into the evolving threat landscape using blog mining technique. Generative AI models have revolutionized the field of cyberattacks, empowering malicious actors to craft convincing and personalized phishing lures, manipulate public opinion through deepfakes, and exploit human cognitive biases. These models, ChatGPT, FraudGPT, and WormGPT, have augmented existing threats and ushered in new dimensions of risk. From phishing campaigns that mimic trusted organizations to deepfake technology impersonating authoritative figures, we explore how generative AI amplifies the arsenal of cybercriminals. Furthermore, we shed light on the vulnerabilities that AI-driven social engineering exploits, including psychological manipulation, targeted phishing, and the crisis of authenticity. To counter these threats, we outline a range of strategies, including traditional security measures, AI-powered security solutions, and collaborative approaches in cybersecurity. We emphasize the importance of staying vigilant, fostering awareness, and strengthening regulations in the battle against AI-enhanced social engineering attacks. In an environment characterized by the rapid evolution of AI models and a lack of training data, defending against generative AI threats requires constant adaptation and the collective efforts of individuals, organizations, and governments. This research seeks to provide a comprehensive understanding of the dynamic interplay between generative AI and social engineering attacks, equipping stakeholders with the knowledge to navigate this intricate cybersecurity landscape.

Gaining an Insight into Learner Satisfaction in MOOCs: An Investigation through Blog Mining

MOOCs can be considered as a powerful alternative in extraordinary situations where people cannot reach formal education. In recent years, the widespread use of the internet worldwide and especially the CoVID-19 has increased the need of people for MOOCs. However, in order to increase the effectiveness of MOOCs, and to provide a better learning environment, the need to evaluate MOOCs has arisen. One of the indicators of quality in online learning is student satisfaction. Accordingly, this research aims to reveal learner satisfaction in MOOCs. The most important indicator for measuring this satisfaction in MOOCs is user comments. In this study, 39101 comments of the participants in 960 MOOCs were examined by using text mining techniques within the framework of satisfaction.

Blog Mining Using Search Engine Optimization and Machine learning

We are providing a platform to Blogger’s and Reader’s. Where the blogger will continually post new content or information in the Blog and readers will check them frequently and gain knowledge or information from the blogs. So, we created a website for both of them, where bloggers have a separate account in the website, they can login and update/write the content and readers can go to the home page directly to view the blogs of bloggers. Here we are creating a Techie Blog website. Keywords- Blog, Blog-mining, Machine learning, Latent Semantic Analysis, Search Engine Optimization, Hummingbird, Panda & Hybridization.

A study of boosted evolutionary classifiers for detecting spam

PurposeEmail is a rapid and cheapest medium of sharing information, whereas unsolicited email (spam) is constant trouble in the email communication. The rapid growth of the spam creates a necessity to build a reliable and robust spam classifier. This paper aims to presents a study of evolutionary classifiers (genetic algorithm [GA] and genetic programming [GP]) without/with the help of an ensemble of classifiers method. In this research, the classifiers ensemble has been developed with adaptive boosting technique.Design/methodology/approachText mining methods are applied for classifying spam emails and legitimate emails. Two data sets (Enron and SpamAssassin) are taken to test the concerned classifiers. Initially, pre-processing is performed to extract the features/words from email files. Informative feature subset is selected from greedy stepwise feature subset search method. With the help of informative features, a comparative study is performed initially within the evolutionary classifiers and then with other popular machine learning classifiers (Bayesian, naive Bayes and support vector machine).FindingsThis study reveals the fact that evolutionary algorithms are promising in classification and prediction applications where genetic programing with adaptive boosting is turned out not only an accurate classifier but also a sensitive classifier. Results show that initially GA performs better than GP but after an ensemble of classifiers (a large number of iterations), GP overshoots GA with significantly higher accuracy. Amongst all classifiers, boosted GP turns out to be not only good regarding classification accuracy but also low false positive (FP) rates, which is considered to be the important criteria in email spam classification. Also, greedy stepwise feature search is found to be an effective method for feature selection in this application domain.Research limitations/implicationsThe research implication of this research consists of the reduction in cost incurred because of spam/unsolicited bulk email. Email is a fundamental necessity to share information within a number of units of the organizations to be competitive with the business rivals. In addition, it is continually a hurdle for internet service providers to provide the best emailing services to their customers. Although, the organizations and the internet service providers are continuously adopting novel spam filtering approaches to reduce the number of unwanted emails, the desired effect could not be significantly seen because of the cost of installation, customizable ability and the threat of misclassification of important emails. This research deals with all the issues and challenges faced by internet service providers and organizations.Practical implicationsIn this research, the proposed models have not only provided excellent performance accuracy, sensitivity with low FP rate, customizable capability but also worked on reducing the cost of spam. The same models may be used for other applications of text mining also such as sentiment analysis, blog mining, news mining or other text mining research.Originality/valueA comparison between GP and GAs has been shown with/without ensemble in spam classification application domain.

Information Seeking and Online Deal Seeking Behavior

Deal-seeking behavior is booming over the last decade. This article aims to identify online deal-seeking resources, understand the current status of online deal-seeking and identify key insights and trends for understanding online deal seeker's behaviors. The authors first conducted an in-depth review of the relevant literature including white papers, survey reports, online news articles, as well as, numerous postings on multiple deal-seeking websites. Then, a blog mining approach is used to mine relevant blog posts they found from the Internet. The article identifies different types of online deal-seeking resources and summarizes key insights and trends for understanding online deal seeker's behaviors. Online deal seeking behavior is a subject that is not well explored. The article summarizes key insights and trends for understanding online deal seeker's behaviors. Information-seeking theories are used to help explain shopper's online deal-seeking behaviors.

Analysing user sentiment of Indian movie reviews

Purpose To be sustainable and competitive in the current business environment, it is useful to understand users’ sentiment towards products and services. This critical task can be achieved via natural language processing and machine learning classifiers. This paper aims to propose a novel probabilistic committee selection classifier (PCC) to analyse and classify the sentiment polarities of movie reviews. Design/methodology/approach An Indian movie review corpus is assembled for this study. Another publicly available movie review polarity corpus is also involved with regard to validating the results. The greedy stepwise search method is used to extract the features/words of the reviews. The performance of the proposed classifier is measured using different metrics, such as F-measure, false positive rate, receiver operating characteristic (ROC) curve and training time. Further, the proposed classifier is compared with other popular machine-learning classifiers, such as Bayesian, Naïve Bayes, Decision Tree (J48), Support Vector Machine and Random Forest. Findings The results of this study show that the proposed classifier is good at predicting the positive or negative polarity of movie reviews. Its performance accuracy and the value of the ROC curve of the PCC is found to be the most suitable of all other classifiers tested in this study. This classifier is also found to be efficient at identifying positive sentiments of reviews, where it gives low false positive rates for both the Indian Movie Review and Review Polarity corpora used in this study. The training time of the proposed classifier is found to be slightly higher than that of Bayesian, Naïve Bayes and J48. Research limitations/implications Only movie review sentiments written in English are considered. In addition, the proposed committee selection classifier is prepared only using the committee of probabilistic classifiers; however, other classifier committees can also be built, tested and compared with the present experiment scenario. Practical implications In this paper, a novel probabilistic approach is proposed and used for classifying movie reviews, and is found to be highly effective in comparison with other state-of-the-art classifiers. This classifier may be tested for different applications and may provide new insights for developers and researchers. Social implications The proposed PCC may be used to classify different product reviews, and hence may be beneficial to organizations to justify users’ reviews about specific products or services. By using authentic positive and negative sentiments of users, the credibility of the specific product, service or event may be enhanced. PCC may also be applied to other applications, such as spam detection, blog mining, news mining and various other data-mining applications. Originality/value The constructed PCC is novel and was tested on Indian movie review data.

Mining graphs from travel blogs: a review in the context of tour planning

The tremendous expansion of user-generated content has made travel planning a tedious activity. The information overload disorients some travel knowledge seekers in choosing preferred places and visiting them in an appropriate order. Hence, a trip planning system mainly deals with collecting popular points of interests (POIs) in a region and proposing routes accordingly. In contrast to the various data sources of tour planning, travel blogs have been paid less attention. The massive text contained in these narratives manifests bloggers’ interactions with places that have been modeled with different graph representations. This review concentrates on the problem of extracting this graph-structured information from travel blogs with an explicit focus on POI graph, the fundamental trip planning structure that facilitates travel decision making. The study first discusses an analysis of travel blog mining and its dominant research themes, based on which three distinct categories of graphs are proposed. Each category of graphs is further explored using practical cases followed with a comparative analysis of existing works including their techniques, dataset and evaluation metrics. With the underlying idea of enhancing travel planning process with better POI graphs, the existing literature is critically assessed at the end along with a number of promising directions for future research.

Investigating mobile cloud learning through blog mining

With the help of cloud computing, Mobile Cloud Learning MCL can overcome many of the obstacles related to mobile learning. It holds considerable promise when it is developed and delivered in the business and education sectors. However, the discussion of MCL is disparate, fragmented, and distributed among different outlets. Systematic, extensively published research on MCL is unavailable. This paper adopts a novel method called blog mining to synthesise the MCL-related discussions in the literature, to provide an in-depth review of MCL, and to identify future trends and challenges to MCL. The findings indicate that, although MCL can offer many benefits to educational institutions and learners, concerns about MCL exist, including security concerns, privacy concerns, vendor lock-in/dependency concerns, price concerns, and concerns about legal/regulatory/information policy consequences. Wireless networks are facing challenges as well.

Research on Structure Ontology Characteristic of Blogosphere

Structure ontology characteristic of blogosphere is one of the hot topics in social computing. It has an important research value for blog dissemination, blog community discovery, blog mining etc. From the empirical research on a number of blogospheres in the real world, we find that the blogosphere is characterized by local centralized large-scale complex social network, whose characteristic is closely related to blog channel, and composed of multiple discrete small social networks. The previous methods by using structure reductionism or functional reductionism cannot fully explain the evolution and development of the blogosphere. Based on the structure ontological viewpoint, This paper presents the blogosphere is composed of belief space, group space and content space. The content space is a information network, the group space is a network of relationships, the belief space is a cultural network, among of which, each one has endogenous rules of evolution, and also interacts with each other, under the common actions of the downward causal relationship and the upward causal relationship, it shows a wealth of structural features.

Using Blog Mining as an Analytical Method to Study the Use of Social Media by Small Businesses

The growth in popularity of blogs opens up new opportunities for conducting information studies. Due to its capability of processing large amount of text data effectively, blog mining can be a valuable method for gaining insights into a given topic. For this study blog mining was used to analyze online blog posts related to social media use by small businesses. This study identified seven relevant themes and found that blog mining is a promising method for understanding emerging Internet-based phenomena. The article also lists some tools for blog mining, discusses the limitations of blog mining, and provides suggestions for overcoming these limitations.

Process Model for Content Extraction from Weblogs

This paper develops and evaluates a BPMN-based process model which identifies and extracts blog content from the web and stores its textual data in a data warehouse for further analyses. Depending on the characteristics of the technologies used to create the weblogs, the process has to perform specific tasks in order to extract blog content correctly. The paper describes three phases: extraction, transformation and loading of data in a repository specifically adapted for blog content extraction. It highlights the objectives in these phases which must be achieved to ensure the correct extraction. The authors integrate the described process in a previously developed framework for blog mining. The authors' process model closes the conceptual gap in this framework as well as the gap in current research of blog mining process models. Furthermore, it can easily be adapted for other web extraction proposals.

Investigating MOOCs through blog mining

<p>MOOCs (massive open online course) is a disruptive innovation and a current buzzword in higher education. However, the discussion of MOOCs is disparate, fragmented, and distributed among different outlets. Systematic, extensively published research on MOOCs is unavailable. This paper adopts a novel method called blog mining to analyze MOOCs. The findings indicate, while MOOCs have benefitted learners, providers, and faculty who develop and teach MOOCs, challenges still exist, such as questionable course quality, high dropout rate, unavailable course credits, ineffective assessments, complex copyright, and limited hardware. Future research should explore the position of MOOCs and how it can be sustained.</p>

Security risks and protection in online learning: A survey

<p>This paper describes a survey of online learning which attempts to determine online learning providers’ awareness of potential security risks and the protection measures that will diminish them. The authors use a combination of two methods: blog mining and a traditional literature search. The findings indicate that, while scholars have identified diverse security risks and have proposed solutions to mitigate the security threats in online learning, bloggers have not discussed security in online learning with great frequency. The differences shown in the survey results generated by the two different methods confirm that online learning providers and practitioners have not considered security as a top priority. The paper also discusses the next generation of an online learning system: a safer personal learning environment which requires a one-stop solution for authentication, assures the security of online assessments, and balances security and usability.</p>

A survey of security risks of mobile social media through blog mining and an extensive literature search

Purpose – As mobile malware and virus are rapidly increasing in frequency and sophistication, mobile social media has recently become a very popular attack vector. The purpose of this paper is to survey the state-of-the-art of security aspect of mobile social media, identify recent trends, and provide recommendations for researchers and practitioners in this fast moving field. Design/methodology/approach – This paper reviews disparate discussions in literature on security aspect of mobile social media though blog mining and an extensive literature search. Based on the detailed review, the author summarizes some key insights to help enterprises understand security risks associated with mobile social media. Findings – Risks related to mobile social media are identified based on the results of the review. Best practices and useful tips are offered to help enterprises mitigate risks of mobile social media. This paper also provides insights and guidance for enterprises to mitigate the security risks of mobile social media. Originality/value – The paper consolidates the fragmented discussion in literature and provides an in-depth review to help researchers understand the latest development of security risks associated with mobile social media.

Interest Mining Algorithm Based on Blog Information

Recently, the amount of blogs on the Internet rises sharply. Hence, mining valuable information in blogs possesses realistic significance for improving user experience, network services, etc. This paper proposes a mining algorithm with blog authors' interests based on classification techniques, which introduces an evaluation standard of non-empty intersection. This algorithm can also improve the hit ratio of recommendation service based on blog authors' interests by means of the interest collection from expanding prediction; therefore, it can reach a higher degree of satisfaction. In addition, this paper performs experiments with the data set from Sina Blog and NetEase Blog, whose result illustrates the higher accuracy of our algorithm. Keywords: Blog mining; Interest mining; Classification algorithm; Non-empty intersection.

Finding keywords in blogs: Efficient keyword extraction in blog mining via user behaviors

Readers are becoming accustomed to obtaining useful and reliable information from bloggers. To make access to the vastly increasing resource of blogs more effective, clustering is useful. Results of the literature review suggest that using linking information, keywords, or tags/categories to calculate similarity is critical for clustering. Keywords are commonly retrieved from the full text, which can be a time-consuming task if multiple articles must be processed. For tags/categories, there is also a problem of ambiguity; that is, different bloggers may define tags/categories of identical content differently. Keywords are important not only to reflect the theme of an article through blog readers’ perspectives but also to accurately match users’ intentions. In this paper, a tracing code is embedded in Blog Connect, a newly developed platform, to collect the keywords queried by readers and then select candidate keywords as co-keywords. The experiments show positive data to confirm that co-keywords can act as a quick path to an article. In addition, co-keyword generation can reduce the complexity and redundancy of full-text keyword retrieval procedures and satisfy blog readers’ intentions.

UNIFIED SEMANTIC BLOG MINING FRAMEWORK AND SUMMARIZED BLOG RETRIEVAL

In today’s scenario, publishing the personal content and technical content has become very easy for the publishers, because of the readily available prominent social media called blog. Content developers, teachers, researchers post various articles relevant to their research or newly emerging topics. Blog content in one blog source may resemble the semantics of blog from other source. Readers, who are fascinated in reading the blog content, anticipate retrieving the blogs from different sources for their query. A large number of posts are available in the web. Hence the blog reader’s task becomes very complex to search the relevant content for their query. This study introduces a novel idea to collect the blog using unified Semantic Blog Mining Framework (SBMF). SBMF collects blogs from different blog sources using the ontology constructed for education domain. Blogs collected from different sources are collection which contains relevant or irrelevant blogs. The new blog summarizer summarizes the blog content and ranks the blogs according to the similarity of the blog with query given by the user. The proposed blog summarizer check the similarity of each sentence in a blog, sort the order of sentence based on the similarity of the text with query word and reduces the number of sentences. The experimental results shows that the proposed unified SBMF and blog summarizer produces better relevant and summarized number of blogs compared to various search engines. SBMF confirms the hundred percentage relevancy compared to other blog search engines. Blog summarizer yields accurate summarization for the collected blogs.

Analyzing the Impact of Earthquake on Tourists�� Activity: Travel Blog Mining

Analyzing the Impact of Earthquake on Tourists�� Activity: Travel Blog Mining

Social media framework for business blog mining

The explosive proliferation of blogs and new forms of social media requires new technologies to transfer the digital realm of the blogosphere into a manageable form. In this paper, we describe a social media data mining framework for business blog mining which evaluates the ability of blog-specific properties to improve classification results in our dataset of business blogs. We find that additional information contained in bloggers and links has a positive influence on our model; however, tags and time tend to have a negative influence. Our findings have implications for future work in blog mining which can leverage blog-specific properties as well as domain knowledge of blog data to improve search and retrieval performance.

Dimensionality reduction framework for blog mining and visualisation

The growing abundance of blogs and new forms of social media has created a critical need for new technologies to transfer the digital realm of social media into a manageable form. Blog mining addresses the domain-specific problem of mining information from blog data. Although mining blogs may share many similarities to web and text documents, existing data mining techniques need to be reevaluated and adapted for the multidimensional representation of blog data, which exhibit dimensions not present in traditional documents. In this paper, a new approach is presented for blog mining and visualisation based on dimensionality reduction techniques. The author-topic model based on latent Dirichlet allocation was extended for analysing and visualising blog authors, links, and time. A framework based on dimensionality reduction is proposed to visualise the blog dimensions of content, tags, authors, links, and time. This framework has been successfully designed, implemented, and evaluated on real-world blog data.