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Data mining is a technique of extracting hidden predictive information from large databases. The important information discovery from the data is more needed in many areas like industries, education, web mining, text mining, etc. Among this text mining is very important in social media. Text mining also referred as the process of deriving information from text. A social media platform such as Twitter...
The exponential development in online social media allows users around the globe the possibility to share and communicate information and ideas freely in different formats of data via internet. This emerging media has become a dominant communication tool and it has been used as a communication channel in several events, especially “The Arab Spring” and BOSTON'S attack etc. In order to develop useful...
Social media platforms facilitate the emergence of citizen communities that discuss real-world events. Their content reflects a variety of intent ranging from social good (e.g., volunteering to help) to commercial interest (e.g., criticizing product features). Hence, mining intent from social data can aid in filtering social media to support organizations, such as an emergency management unit for...
Instagram is one of the popular social media applications used by a wide range of people around the world. The significant growth of active Instagram users affects the size of Instagram data. The more number of users, the larger and more various Instagram data is posted. In line with its popularity, in recent years many researchers begin to study and analyze it for various purposes, such as detecting...
Social media is widely used as a channel of communication in general purposes, including the comment that are related to retail business. It is a highly effective communication tool for direct interacting with their customers. Growth rate of the users is rapidly increasing, because they use this channel to receive information and share something interesting. In this paper, we present a comparison...
Many threats in the real world can be related to activities in public sources on the internet. Early detection of threats based on internet information could assist in the prevention of incidents. However, the amount of data in social media, blogs and forums rapidly increases and it is time consuming for security services to monitor all these sources. Therefore, it is important to have a system that...
This article examines how different social media platforms affect opinion composition and evolution. We differentiate between product and non-product oriented outlets as they differ in the salience of social cues, thus resulting in distinct user behaviours. We extend prior research in several ways. First, comparing between comments from different types of social media platforms, we show that the product...
With the growth in social media, online forums have become major sources of data for social network analysis and a major research area in data analytics. However, many aspects of social networks have not been addressed fully, if at all. First, there is the need to capture various parts of the content of the exchanges, for example, bare text and hashtags, which constitute some of the major features...
This minitrack encompasses papers of a quantitative, theoretical or applied nature that focus on: Content Mining of Social Media -- discovery of patterns from the text, images, audio, video and other data generated by Social Media sites Structure Mining of Social Media -- social network analysis of the node and connection (graph) structures underlying Social Media sites
This paper describes a system which surveys the French Presidential Election trends from Twitter's discussions. This system carries out the automatic collection, evaluation and rating of tweets for evaluating the trends. The objective of this paper is to discuss the variety of issues and challenges surrounding the perspectives regarding the use of Social Network Analyses and Text Mining methods for...
With an increasing interest in social media, a large number of Web images have been created on the Internet. Therefore, extracting useful knowledge from a large-scale set of Web images has become a new type of challenge. In this paper, we focus on Web images that have been posted onto the Internet through social media sites. The main objective of this study is to extract the events and track the topics...
Since the textual contents on online social media are highly unstructured, informal, and often misspelled, existing research on message-level offensive language detection cannot accurately detect offensive content. Meanwhile, user-level offensiveness detection seems a more feasible approach but it is an under researched area. To bridge this gap, we propose the Lexical Syntactic Feature (LSF) architecture...
The quantity of medical publications in the media such a tweets, blogs, and another content in social media is growing at an exponential rate. Exploring and analyzing this content has become a necessity. The objective of this paper is to discuss the variety of issues and challenges surrounding the perspectives regarding the use of Social Network Analyses and Text Mining methods for applications in...
The extraction of meaning or at least inter-dependencies using data and text mining methods is well understood. Numerous approaches have been taken to select relevant information from often very large data sets. The discarding of items that are not relevant to a parameterized retrieval is usually based on an 'include or do not include' decision imbedded in some kind of branch-and-bound algorithm,...
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