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Social Media Big Data have transformed the scale of exploratory analysis on the web and offered new means of performing tasks that were not feasible before. Over a half century ago, Milgram showed that the average number of intermediaries (called the "degrees of separation") between two individuals is
Social networks like Twitter and Facebook have gained a significant popularity with people from all parts of the society in the past decade, providing a new kind of data source for novel social-aware applications. A great majority of the users are online all the time, posting real-time information on various topics
interest for people so in this paper an easy approach of gathering and analyzing data through keyword based search in social networks is examined using NodeXL and data is gathered from twitter in which political trends have been analyzed. As a result it will be analyzed that, what people are focusing most in politics.
In this paper we present the work done on social media analysis to predict civil unrest using keyword filtering. The information given on the social media is delivered to every person within the fraction of seconds. This rapid circulation of information and the people opinions through social platform affects or create
We examine whether aggregate daily Twitter keyword volumes over eight months from November 2011 to June 2012 can be used to predict aggregate daily consumer spending as reported by Gallup. We also examine whether Twitter keyword volume improves predictive ability over prediction based solely on current spending
Recently, one of the most popular applications on the Internet is the social networking, such as, Facebook and Twitter. Finding resources using recent Peer-to-Peer (P2P) schemes in a social network has limits, such as distance problem between peers etc. So, in this paper, we present an efficient social P2P management
) based method to determine informative tweet and the real-time event detection algorithm to detect the timely occurrence of the given event. In this study, CNN model trained from the tweets related to the earthquake in the past labeled by crowdsourcing plays a role as the classifier to predict an earthquake keyword related
keyword search interface or a get-neighbors (e.g., Friends) interface. Many interesting queries cannot be directly answered by any of these interfaces, e.g., Find Twitter users in Los Angeles that have tweeted the word "diabetes" in the last year. Note that the Twitter programming interface does not allow
media topics associated with each topic's keyword analysis. In our research we further explored the potential usage and application of social media analytics tools within local government and found that social media analytics can be of great value for the government in both special events and routine activities. Major
Terrorists communicate and disseminate their activitiesusing social media, such as Twitter, where complex networksof user accounts are formed and need to be effectively analysedby Law Enforcement Agencies (LEAs). To this end, we proposea novel visualisation tool that assists intelligence analysts andinvestigators
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
this problem by automatically dividing the social network of a Twitter user into personal cliques, and annotating each clique with keywords to identify the common ground of a clique. Our proposed clique annotation method extracts keywords from the tweet history of the clique members and individually weights the extracted
Web applications are increasingly showing recommended users from social media along with some descriptions, an attempt to show relevancy-why they are being shown. For example, Twitter search for a topical keyword shows expert twitterers on the side for `whom to follow'. Google+ and Facebook also recommend users to
Social media offer abundant information for studying people's behaviors, emotions and opinions during the evolution of various rare events such as natural disasters. It is useful to analyze the correlation between social media and human-affected events. This study uses Hurricane Sandy 2012 related Twitter text data to
measure sentiment using a binary choice keyword algorithm and a multi-knowledge based approach is proposed using, Self-Organizing Maps and tourism domain knowledge in order to model sentiment. We develop a visual model to express this taxonomy of sentiment vocabulary and then apply this model to maximums and minimums in the
We demonstrate the effectiveness that machine learning can bring to improving social media platforms through a case study on Twitter trending topics. Social media relies heavily on tagging and often does not take advantage of machine learning advances. Twitter is no exception. Individual tweets are identified as being
Applying data mining techniques to social media can yield interesting perspectives to understanding individual and human behavior, detecting hot issues and topics, or discovering a group and community. However, it is difficult to gather the data related to a specific topic due to the main characteristics of social
number of keywords for the searches of the events, especially in relation with analytics and searches on SNs for the reflectance of those events. A special attention is given to synonyms.
data for the social science research. The system comprises four main functions including: (i) case study management, (ii) user/keyword search, (iii) interest group customization, and (iv) user-friendly analysis and visualization. Furthermore, three kinds of measures: connectivity, reciprocity, and mentioning, are
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