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    Emotion Dynamics of Public Opinions on Twitter

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    [EN] Recently, social media has been considered the fastest medium for information broadcasting and sharing. Considering the wide range of applications such as viral marketing, political campaigns, social advertisement, and so on, influencing characteristics of users or tweets have attracted several researchers. It is observed from various studies that influential messages or users create a high impact on a social ecosystem. In this study, we assume that public opinion on a social issue on Twitter carries a certain degree of emotion, and there is an emotion flow underneath the Twitter network. In this article, we investigate social dynamics of emotion present in users' opinions and attempt to understand (i) changing characteristics of users' emotions toward a social issue over time, (ii) influence of public emotions on individuals' emotions, (iii) cause of changing opinion by social factors, and so on. We study users' emotion dynamics over a collection of 17.65M tweets with 69.36K users and observe 63% of the users are likely to change their emotional state against the topic into their subsequent tweets. Tweets were coming from the member community shows higher influencing capability than the other community sources. It is also observed that retweets influence users more than hashtags, mentions, and replies.The work described in this article was carried out in the OSiNT Lab (https://www.iitg.ac.in/cseweb/osint/), Indian Institute of Technology Guwahati, India. The creation of the dataset used in this study was partly supported by the Ministry of Information and Electronic Technology, Government of India.Naskar, D.; Singh, SR.; Kumar, D.; Nandi, S.; Onaindia De La Rivaherrera, E. (2020). Emotion Dynamics of Public Opinions on Twitter. ACM Transactions on Information Systems. 38(2):1-24. https://doi.org/10.1145/3379340124382Ahmed, S., Jaidka, K., & Cho, J. (2016). 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    Tracing Public Opinion Propagation and Emotional Evolution Based on Public Emergencies in Social Networks

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    Social network has become the main communication platform for public emergencies, and it has also made the public opinion influence spread more widely. How to effectively obtain public opinions from it to guide the healthy development of the society is an important issue that the government and other functional departments are concerned about. However, the interaction and evolution mechanism between the subject and the environment in the public opinion propagation is complicated, and the public and media attention and reaction to the incident are closely linked with the progress of the incident disposal. And public mining corpus has some shortcomings in the distribution of emotional classification. Only the timely update of artificial rules and emotional dictionary resources, it can handle new text data well. In fact, from the perspective of public opinion propagation, this paper built the network matrix between Internet users through the forwarding relationship, and used the social network analysis method and the emotion mining analysis technology to study the interaction and evolution mechanism between the subject and the environment in the public opinion propagation, and it studied the role of users in the emotional propagation of social networks. This paper proposed a sentiment analysis method on the micro-blog platform, which expanded the emotional dictionary and took sentence and emoticon and sentence patterns into account, which improved the accuracy of positive and negative classifications and emotional polarity analysis of the micro-blog

    Análisis de la influencia significativa de la depresión de los estudiantes utilizando las redes neuronales y las técnicas del árbol de clasificación

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    Students”™ depression is an important issue to most of the higher learning institutions. Although this issue has been investigated by many researchers using statistical analysis and data mining techniques, this paper focused on the performance of Classification Tree and Artificial Neural Network techniques of depression among Engineering Technology students at Universiti Kuala Lumpur (UniKL) Malaysian Institute of Information Technology (MIIT). Various factors that may likely influence the students”™ depression were identified. Stress factors, social factors (interpersonal and intrapersonal), environment factor as well as demographic factors attribute to predict the students”™ depression. The performances of these techniques are compared, based on accuracy. From the findings of the analysis, social intra-personal stress was found significantly contribute to students”™ depression. Performances of both methods were compared using cross validation analysis. Artificial Neural Network has the least of error rate and has the highest accuracy; therefore, Artificial Neural Network is the best technique to classify in this data set.La depresión de los estudiantes es un tema importante para la mayorí­a de las instituciones de educación superior. Aunque este problema ha sido investigado por muchos investigadores que utilizan técnicas de análisis estadí­stico y de minerí­a de datos, este documento se centró en el rendimiento de las técnicas de depresión de las redes de neuronas artificiales y de árboles de clasificación entre estudiantes de Tecnologí­a de la Ingenierí­a en la Universidad de Kuala Lumpur (UniKL) Instituto Malasio de Tecnologí­a de la Información ( MIIT). Se identificaron varios factores que pueden influir en la depresión de los estudiantes. Factores de estrés, factores sociales (interpersonales e intrapersonales), factores ambientales y factores demográficos atribuidos para predecir la depresión de los estudiantes. Se comparan los rendimientos de estas técnicas, en función de la precisión. A partir de los resultados del análisis, se encontró que el estrés intrapersonal social contribuyó significativamente a la depresión de los estudiantes. Los rendimientos de ambos métodos se compararon mediante análisis de validación cruzada. La red neuronal artificial tiene la menor tasa de error y la más alta precisión; por lo tanto, la red neuronal artificial es la mejor técnica para clasificar en este conjunto de datos

    The dominant of Bloggers in Malaysian politics through social networks

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    Every country in this world has own political issues. In Malaysia for example, political issues played an important role that can influence other factors such as social and economy. As we all know, political factor can give positive and negative effect to a situation in Malaysia. The frequent usage of computer nowadays by Malaysian people helps in spreading information and news about political situation in Malaysia through cyberspace. In this paper, we use web mining system with Artificial Immune System (AIS) to regain a small group of relevant websites and webpages on political issues in Malaysia. To analyze the relationship between website and webpages, the concept of social networks will be used. Result from the web mining system with AIS will be used to understand the impact of social network to the political situation in Malaysia

    Finding Influential Users in Social Media Using Association Rule Learning

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    Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods
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