Spam Detection in Social Media Employing Machine Learning Tool for Text Mining

Sharmin, Sadia; Zaman, Zakia

doi:10.1109/sitis.2017.32

Cited by 24 publications

(15 citation statements)

References 3 publications

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“…Machine learning algorithms are used to analyze the behavior of the user via real-time analysis of the content browsed by them, and relevant online advertisements are recommended accordingly. Moreover, the detection of spam using data mining techniques also employs the use of machine learning [138]. In addition, Hadoop and machine learning algorithms are used by banks for analysis of loan data to check the reliability of lending organizations, thereby increasing profitability and innovation [139].…”

Section: Applications Of Big Data and Pertinent Discussionmentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

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Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

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Section: Applications Of Big Data and Pertinent Discussionmentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

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“…Many machine learning algorithms, including logistic regression (LR), naïve Bayes (NB), support vector machine (SVM), K-nearest neighbor (KNN) and ensemble classifiers (such as bagging and random forest (RF)), have been widely used in text classification studies (Sebastiani, 2002; Liu et al , 2017; Sharmin and Zaman, 2017; Cichosz, 2018; Gravanis et al , 2019). For example, a total of 2000 teachers' posts were collected and coded for constructing six-class classification models based on NB and SVM to classify the teachers' reflective thinking in the online learning environment (Liu et al , 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The experiment's results showed that the NB classifier outperformed SVM. Sharmin and Zaman (2017) transformed 1956 messages into vectors based on the TF-IDF approach, and then built binary classification models based on NB, KNN, SVM and bagging to predict whether a text was spam. Cichosz (2018) collected 122,463 posts from a Polish discussion forum and then trained binary classification models based on LR, multinomial naı €ve Bayes (MNB), SVM and RF algorithms to examine the effects of two different vector text representations.…”

Section: Semantic Information Extraction In Text Classificationmentioning

confidence: 99%

“…Then, these vector representations can be used as inputs of traditional machine learning or as deep learning models for extracting deep semantic features to achieve text classification. Thus far, many text classification applications have emerged, and they have obtained remarkable results, such as the prediction of writing quality (Kyle and Crossley, 2016), spam detection (Sharmin and Zaman, 2017), fake news detection (Gravanis et al , 2019), sentiment analysis (Abdi et al , 2019) and question answering (Bordes et al , 2014). However, it has been found that existing text classification studies have mainly adopted one type of feature rather than the combination of the semantic and linguistic as well as psychological information.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing online discussion data for understanding the student's critical thinking

Yang

Hung

et al. 2021

DTA

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PurposeCritical thinking is considered important in psychological science because it enables students to make effective decisions and optimizes their performance. Aiming at the challenges and issues of understanding the student's critical thinking, the objective of this study is to analyze online discussion data through an advanced multi-feature fusion modeling (MFFM) approach for automatically and accurately understanding the student's critical thinking levels.Design/methodology/approachAn advanced MFFM approach is proposed in this study. Specifically, with considering the time-series characteristic and the high correlations between adjacent words in discussion contents, the long short-term memory–convolutional neural network (LSTM-CNN) architecture is proposed to extract deep semantic features, and then these semantic features are combined with linguistic and psychological knowledge generated by the LIWC2015 tool as the inputs of full-connected layers to automatically and accurately predict students' critical thinking levels that are hidden in online discussion data.FindingsA series of experiments with 94 students' 7,691 posts were conducted to verify the effectiveness of the proposed approach. The experimental results show that the proposed MFFM approach that combines two types of textual features outperforms baseline methods, and the semantic-based padding can further improve the prediction performance of MFFM. It can achieve 0.8205 overall accuracy and 0.6172 F1 score for the “high” category on the validation dataset. Furthermore, it is found that the semantic features extracted by LSTM-CNN are more powerful for identifying self-introduction or off-topic discussions, while the linguistic, as well as psychological features, can better distinguish the discussion posts with the highest critical thinking level.Originality/valueWith the support of the proposed MFFM approach, online teachers can conveniently and effectively understand the interaction quality of online discussions, which can support instructional decision-making to better promote the student's knowledge construction process and improve learning performance.

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“…77 Classification Twitter and myspace social data spam detection using Naive Bayes and Support vector machine classifier. 78…”

Section: Field Of Usage Mapping and Assemblymentioning

confidence: 99%

Pattern Mining Approaches Used in Social Media Data

Chaki

Dey²,

Panigrahi

et al. 2020

Int. J. Unc. Fuzz. Knowl. Based Syst.

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Social media conveys a reachable platform for users to share information. The inescapable practice of social media has produced remarkable volumes of social data. Social media gathers the data in both structured-unstructured and formal-informal ways as users are not concerned with the exact grammatical structure and spelling when interacting with each other by means of various social networking websites (Twitter, Facebook, YouTube, LinkedIn, etc.). People are increasingly involved in and dependent on social media networks for data, news and opinions of other handlers on a variety of topics. The strong dependence on social media network sites contributes to enormous data generation characterized by three issues: scale, noise, and variety. Such problems also hinder social network data to be evaluated manually, resulting in the correct use of statistical analytical methods. Mining social media data can extract significant patterns that can be advantageous for consumers, users, and business. Pattern mining offers a wide variety of methods to detect valuable knowledge from huge datasets, such as patterns, trends, and rules. In this work, data was collected comprised of users’ opinions and sentiments and then processed using a significant number of pattern mining methods. The results were then further analyzed to attain meaningful information. The aim of this paper is to deliver a summary and a set of strategies for utilizing the ubiquitous pattern mining approaches, and to recognize the challenges and future research guidelines of dealing out social media data.

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Spam Detection in Social Media Employing Machine Learning Tool for Text Mining

Cited by 24 publications

References 3 publications

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Analyzing online discussion data for understanding the student's critical thinking

Pattern Mining Approaches Used in Social Media Data

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