• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.206-211
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• 2021

Internet users are increasingly invited to express their opinions on various subjects in social networks, e-commerce sites, news sites, forums, etc. Much of this information, which describes feelings, becomes the subject of study in several areas of research such as: "Sensing opinions and analyzing feelings". It is the process of identifying the polarity of the feelings held in the opinions found in the interactions of Internet users on the web and classifying them as positive, negative, or neutral. In this article, we suggest the implementation of a sentiment analysis tool that has the role of detecting the polarity of opinions from people about COVID-19 extracted from social media (tweeter) in the Arabic language and to know the impact of the pre-processing phase on the opinions classification. The results show gaps in this area of research, first of all, the lack of resources when collecting data. Second, Arabic language is more complexes in pre-processing step, especially the dialects in the pre-treatment phase. But ultimately the results obtained are promising.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.93-98
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• 2023

Instagram is one of the fastest-growing online photo social web services where users share their life images and videos with other users. Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. Hashtags can be used on just about any social media platform, but they're most popular on Twitter and Instagram. Using hashtags is essentially a way to group together conversations or content around a certain topic, making it easy for people to find content that interests them. Practically on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stophashtags, that are used across totally different images just for gathering clicks and for search ability enhancement. Hence in this work, Sorting instagram hashtags all the way through mass tagging using HITS (Hyperlink-Induced Topic Search) algorithm is presented. The hashtags can sorted to several groups according to Jensen-Shannon divergence between any two hashtags. This approach provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hashtags per image.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.155-162
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• 2021

The expansion of the world wide web has led to a huge amount of user generated content over different forums and social media platforms, these rich data resources offer the opportunity to reflect, and track changing public sentiments and help to develop proactive reactions strategies for decision and policy makers. Analysis of public emotions and opinions towards events and sentimental trends can help to address unforeseen areas of public concerns. The need of developing systems to analyze these sentiments and the topics behind them has emerged tremendously. While most existing works reported in the literature have been carried out in English, this paper, in contrast, aims to review recent research works in Arabic language in the field of thematic sentiment analysis and which techniques they have utilized to accomplish this task. The findings show that the prevailing techniques in Arabic topic-based sentiment analysis are based on traditional approaches and machine learning methods. In addition, it has been found that considerably limited recent studies have utilized deep learning approaches to build high performance models.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.261-266
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• 2021

The spread of cloud computing, digital computing, and the popular social media platforms have led to increased growth of data. That growth of data results in what is known as big data (BD), which seen as one of the most strategic resources. The analysis of these BD has allowed generating value from massive raw data that helps in making effective decisions and providing quality of service. With Vision 2030, Saudi Arabia seeks to invest in BD technologies, but many challenges and barriers have led to delays in adopting BD. This research paper aims to search in the state of Big Data Analytics (BDA) in Saudi higher education sector, identify the barriers by reviewing the literature, and then to apply the design-reality gap model to assess these barriers that prevent effective use of big data and highlights priority areas for action to accelerate the application of BD to comply with Vision 2030.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.112-119
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• 2021

Social media has become a global means of communication in people's lives. Most people are using Twitter for communication purposes and its inappropriate use, which has negative effects on people's lives. One of the widely common misuses of Twitter is cyberbullying. As the resources of dialectal Arabic are rare, so for cyberbullying most people are using dialectal Arabic. For this reason, the ultimate goal of this study is to detect and classify cyberbullying on Twitter in the Arabic context in Saudi Arabia. To help in the detection and classification of tweets, Pointwise Mutual Information (PMI) to generate a lexicon, and Support Vector Machine (SVM) algorithms are used. The evaluation is performed on both methods in terms of the F1-score. However, the F1-score after applying the PMI is 50%, while after the SVM application on the resampling data it is 82%. The analysis of the results shows that the SVM algorithm outperforms better.

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.101-109
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• 2023

This paper discusses the use of sentiment analysis and text summarization techniques to extract valuable information from the large volume of user-generated content such as reviews, comments, and feedback on online platforms and social media. The paper highlights the effectiveness of sentiment analysis in identifying positive and negative reviews and the importance of summarizing such text to facilitate comprehension and convey essential findings to readers. The proposed work focuses on summarizing all positive and negative reviews to enhance product quality, and the performance of the generated summaries is measured using ROUGE scores. The results show promising outcomes for the developed methods in summarizing user-generated content.

• 시큐리티연구
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• 제32호
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• pp.33-63
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• 2012

인간은 경제적 요인, 물리적 환경 요인, 사회적 환경요인, 미디어에 의한 영향 등 이루 셀 수 없을 만큼 많은 요인에 의해 삶의 활동에 영향을 받는다. 특히, 범죄 발생으로 인한 범죄에 대한 두려움의 발생은 인간의 활동영역을 축소시키는데 영향을 주고 있다. 범죄에 대한 두려움은 직접적인 범죄피해경험, 주변의 풍문 혹은 미디어에 의한 간접적인 범죄피해 사실 인지, 주변 환경의 무질서 목격 등으로 인하여 범죄에 대한 두려움이 발생하게 된다. 따라서, 범죄에 대한 두려움을 감소시키기 위해서는 범죄를 예방하여 안전한 환경을 조성해야 하지만, 사회가 발전함에 따른 범죄발생의 증가에 비해 경찰의 범죄예방 활동은 그에 따라가지 못해 문제가 발생하고 있다. 이러한 문제를 해결하기 위해 민간경비 활동을 통해 범죄예방을 하고 있는데, 실제 민간 경비를 선택함에 있어서 범죄에 대한 두려움이 영향을 주는 요인에 대해서 이 연구를 통해 살펴보기로 한다. 연구의 목적을 달성하기 위해서 이 연구에서는 2011년 8월 한달 동안 서울과 대구의 '담장허물기' 운동을 실시하여 자연적 감시를 확보한 단독주택 100호를 무작위로 선정하여, 단독주택 거주자를 설문을 통해 범죄에 대한 두려움이 민간경비 선택에 미치는 영향에 대해서 살펴보았다. 그 결과, 범죄에 대한 두려움에 영향을 주는 요인인 직접적이 범죄피해경험, 간접적인 범죄피해경험, 무질서 중에서 직 간접적인 요인에 의한 범죄두려움은 민간경비 선택에 영향을 준다고 나타났다.

• 한국산학기술학회논문지
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• 제11권9호
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• pp.3239-3248
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• 2010

본 연구의 목적은 전자결제의 서비스품질과 사회화요인, 자기효능감의 조절효과를 통해 전자결제의 수용의도에 미치는 영향력을 분석하고자 한다. 이를 위해서 구조화된 설문지를 이용하여 2,331명의 전자결제 사용경험자로부터 자료를 수집하였고, 수집된 자료는 회귀분석을 이용하여 분석하였다. 그 결과, 보안성, 반응성, 매스미디어, 구전, 보안성과 자기효능감의 상호작용이 전자결제 수용의도에 정(+)적인 영향을 미친 것으로 나타났다. 이러한 연구 결과를 통해 전자결제 수용의도의 영향력을 단순히 서비스품질만을 통해 설명하는 것보다 사회화의 영향력 및 자기효능감의 조절변수를 통해 설명하는 것이 더 적합하다는 것이 증명되었다.

• 정보화정책
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• 제23권4호
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• pp.76-94
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• 2016

최근 정보보호 분야에서는 사회공학, 랜섬웨어와 같은 정보보호 기술만으로는 막을 수 없는 공격이 증가하고 있으며, 이에 따라 정보보호인식의 중요성이 부각되고 있다. 또한 정보보호 업계의 수익악화가 두드러짐에 따라 정보보호 업계의 신성장동력을 탐색하고 해외시장을 개척하고자 하는 노력이 증대 되고 있다. 이에 따라 본 연구는 사람들이 생각하는 정보보호 관련 이슈들을 도출하고, 온라인에서의 정보보호 관련 이슈의 국가간 비교 분석을 통하여 한국의 정보보호인식의 개선방안을 제안하고자 한다. 이를 위해 본 연구에서는 토픽 모델링 기법을 적용하여 한국과 미국, 중국의 정보보호 관련 이슈를 확인 하고, 감성 분석을 통하여 점수를 측정해 비교 분석하였다. 본 연구의 학술적 시사점은 비정형 데이터인 트위터의 트윗을 텍스트 마이닝 기법인 토픽 모델링과 감성 분석 기법을 통해 분석하고, 도출된 이슈를 기반으로 국가간 비교 연구를 수행 하였으며 이를 바탕으로 한국의 정보보호인식 강화 방안을 탐색하였다는 점에서 의의가 있다. 또한 본 연구의 실무적 시사점은 트위터 API를 통한 실제 데이터를 이용한 연구로 본 연구 모델을 활용하여 국내 이슈 및 해외 시장 분석에 활용 가능할 것 이라는 점에 있다.