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  • Open Access

    ARTICLE

    Autonomous Eyewitness Identification by Employing Linguistic Rules for Disaster Events

    Sajjad Haider*, Muhammad Tanvir Afzal

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 481-498, 2021, DOI:10.32604/cmc.2020.012057

    Abstract Social networking platforms provide a vital source for disseminating information across the globe, particularly in case of disaster. These platforms are great mean to find out the real account of the disaster. Twitter is an example of such platform, which has been extensively utilized by scientific community due to its unidirectional model. It is considered a challenging task to identify eyewitness tweets about the incident from the millions of tweets shared by twitter users. Research community has proposed diverse sets of techniques to identify eyewitness account. A recent state-of-the-art approach has proposed a comprehensive set… More >

  • Open Access

    ARTICLE

    Finding Temporal Influential Users in Social Media Using Association Rule Learning

    Babar Shazad1, Hikmat Ullah khan2, Zahoor-ur-Rehman1, Muhammad Farooq2, Ahsan Mahmood1, Irfan Mehmood3,*, Seungmin Rho3, Yunyoung Nam4,*

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 87-98, 2020, DOI:10.31209/2019.100000130

    Abstract The social media has become an integral part of our daily life. The social web users interact and thus influence each other influence in many aspects. Blogging is one of the most important features of the social web. The bloggers share their views, opinions and ideas in the form of blog posts. The influential bloggers are the leading bloggers who influence the other bloggers in their online communities. The relevant literature presents several studies related to identification of top influential bloggers in last decade. The research domain of finding the top influential bloggers mainly focuses… More >

  • Open Access

    ARTICLE

    Building Ontology for Different Emotional Contexts and Multilingual Environment in Opinion Mining

    Wan Taoa,b, Tao Liua,b

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 65-72, 2018, DOI:10.1080/10798587.2016.1267243

    Abstract With the explosive growth of various social media applications, individuals and organizations are increasingly using their contents (e.g. reviews, forum discussions, blogs, micro-blogs, comments, and postings in social network sites) for decision-making. These contents are typical big data. Opinion mining or sentiment analysis focuses on how to extract emotional semantics from these big data to help users to get a better decision. That is not an easy task, because it faces many problems, such as different context may make the meaning of the same word change variously, at the same time multilingual environment restricts the More >

  • Open Access

    ARTICLE

    Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic

    Ibrahim Arpaci1, Shadi Alshehabi2, Mostafa Al-Emran3, *, Mahmoud Khasawneh4, Ibrahim Mahariq4, Thabet Abdeljawad5, 6, 7, Aboul Ella Hassanien8, 9

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 193-204, 2020, DOI:10.32604/cmc.2020.011489

    Abstract People started posting textual tweets on Twitter as soon as the novel coronavirus (COVID-19) emerged. Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks. Therefore, this study aimed to analyze 43 million tweets collected between March 22 and March 30, 2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis. The results indicated that unigram terms were trended more frequently than bigram and trigram terms. A large number of tweets about the COVID-19 were disseminated and received widespread public attention… More >

  • Open Access

    ARTICLE

    Why Ignore the Dark Side of Social Media? A Role of Social Media in Spreading Corona-Phobia and Psychological Well-Being

    Saqib Amin*

    International Journal of Mental Health Promotion, Vol.22, No.1, pp. 29-38, 2020, DOI:10.32604/IJMHP.2020.011115

    Abstract Coronaviruses are a category of associated viruses that trigger disease in mammals and birds. Human coronaviruses have been identified including severe acute respiratory syndrome-related coronavirus (SARS-CoV) in 2003, human coronavirus NL63 (HCoV NL63) in 2004, human coronavirus HKU1 (HKU1) in 2005, Middle East respiratory syndrome-related coronavirus (MERSCoV) in 2012, and severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) in December, 2019. This study aims to examine whether social media at residing/admittance in quarantine ward (due to corona virus pandemic disease) affects psychological health or not? We asked questions from 250 quarantined patients infected from coronavirus (restricted More >

  • Open Access

    ARTICLE

    Media and Mental Health Literacy: Do Mediated Interventions Enhance Mental Health Awareness? Implications and Policy Recommendations

    Arooj Arshad1,*, Mian Ahmad Hanan2,*, Noshina Saleem3, Saima Farzooq4, Remsha Fatima5

    International Journal of Mental Health Promotion, Vol.21, No.3, pp. 99-109, 2019, DOI:10.32604/IJMHP.2019.010834

    Abstract In the current digital era, public health campaigns using media has been very successful in giving knowledge and changes the attitudes of people. But till now scarce literature is available related to media campaigns about mental health. In this study Pre-Post Quasi Experimental Design using vignettes as a data collection measure were employed. The participants were categories in to experimental (n = 138) and control (n = 134) groups having 18–55 years of age to evaluate the efficiency of media mediated interventions using social media campaign in increasing Mental Health Literacy (MHL). The results from More >

  • Open Access

    ARTICLE

    Network Embedding-Based Anomalous Density Searching for Multi-Group Collaborative Fraudsters Detection in Social Media

    Chengzhang Zhu1, 2, Wentao Zhao2, *, Qian Li1, Pan Li2, Qiaobo Da3

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 317-333, 2019, DOI:10.32604/cmc.2019.05677

    Abstract Detecting collaborative fraudsters who manipulate opinions in social media is becoming extremely important in order to provide reliable information, in which, however, the diversity in different groups of collaborative fraudsters presents a significant challenge to existing collaborative fraudsters detection methods. These methods often detect collaborative fraudsters as the largest group of users who have the strongest relation with each other in the social media, consequently overlooking the other groups of fraudsters that are with strong user relation yet small group size. This paper introduces a novel network embedding-based framework NEST and its instance BEST to… More >

  • Open Access

    ARTICLE

    Modeling and Predicting of News Popularity in Social Media Sources

    Kemal Akyol1,*, Baha Şen2

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 69-80, 2019, DOI:10.32604/cmc.2019.08143

    Abstract The popularity of news, which conveys newsworthy events which occur during day to people, is substantially important for the spectator or audience. People interact with news website and share news links or their opinions. This study uses supervised learning based machine learning techniques in order to predict news popularity in social media sources. These techniques consist of basically two phrases: a) the training data is sent as input to the classifier algorithm, b) the performance of pre-learned algorithm is tested on the testing data. And so, a knowledge discovery from the data is performed. In… More >

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