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Abstract. In the context of smart cities and Internet of Things (IoT), there are many trending contents on the social networks that reflect the picture of the community or their interest. In this paper, we propose a model that automatically collect trending social data and analyze them automatically. The model explores trending contents, overall attitude of textual contents and the relationships among...
We study the discursive practices of politicians and journalists on social media. For this we need more annotated data than we currently have but the annotation process is time-consuming and costly. In this paper we examine machine learning methods for automatically annotating unseen tweetsbased on a small set of manually annotated tweets. Forimproving the performance of the learner, we focus onmethods...
Seasonal influenza is a contagious respiratory illness that can cause various complications, worsen chronic illnesses, and sometimes lead to deaths. During 2009 H1N1 flu pandemic, up to 203,000 deaths occurred worldwide. Early detection and prediction of disease outbreak is critical because it can provide more time to prepare a response and significantly reduce the impact caused by a pandemic. The...
With data storage and processing technology developing fast, there has been accumulated a great amount of open data that comes from everywhere including social media. One of the promising tools to analyze these data is fuzzy cognitive maps that help to describe connections and substances to reveal patterns, facts and knowledge. One of the problems when creating cognitive maps is the identification...
As multi-dimensional text data are being generated at dazzling rate, topic modelling has become an important instrument for learning from large unstructured document sets. To focus on specific subsets of large document corpora, a user may specify various criteria to identify documents of interest before extracting topics from the documents. In this paper, we aim to accelerate the computation of topic...
The prosperity of online rating system makes it an important place for malicious vendors to mislead public's online decisions, whereas the security related studies are lagging behind. In this work, we adopt a quantile regression model to investigate influential factors on online user choices and reveal the “self-exciting” property of online market. Inspired by these findings, we propose a novel iterative...
This paper introduces a novel framework for modeling temporal events with complex longitudinal dependency that are generated by dependent sources. This framework takes advantage of multidimensional point processes for modeling time of events. The intensity function of the proposed process is a mixture of intensities, and its complexity grows with the complexity of temporal patterns of data. Moreover,...
Social media has become very popular over the past decade. There are millions of users across the world sharing information with each other instantaneously through several social media platforms. With these many users sharing huge volumes of data analysis of social media data has become a prominent area of research. Recent studies on the use of data from social media platforms such as Twitter for...
Thousands of news are published everyday reporting worldwide events. Most of these news obtain a low level of popularity and only a small set of events become highly popular in social media platforms. Predicting rare cases of highly popular news is not a trivial task due to shortcomings of standard learning approaches and evaluation metrics. So far, the standard task of predicting the popularity of...
In the recent years, the number of social media, such as Facebook, LinkedIn, Tumblr have been increasing regularly, have become ideal platforms for a large number of users at lower costs. However, social media helped companies get known by a wider public and collect the relevant information's about customers, their needs and their expectations. Therefore, several decision makers have worked on these...
Mozambique has been affected by multiple conflicts since colonial rule. This paper proposes an e-PAZ Early Warning System that helps in identifying potential conflicts. The system filters conflict related news from social media. It also offers geographic and socioeconomic information of the conflict zone. It provides qualitative and quantitative analysis on the past conflicts and gives user an open...
Social media provides increasing opportunities for users to voluntarily share their thoughts and concerns in a large volume of data. While user-generated data from each individual may not provide considerable information, when combined, they include hidden variables, which may convey significant events. In this paper, we pursue the question of whether social media context can provide socio-behavior...
Social Media generates information about news and events in real-time. Given the vast amount of data available and the rate of information propagation, reliably identifying events is a challenge. Most state-of-the-art techniques are post hoc techniques that detect an event after it happened. Our goal is to detect onset of an event as it is happening using the user-generated information from Twitter...
These days, social media has played a significant role in daily life of all people and ages in order to communicate as well as express their thoughts and feelings. In this paper, the authors have studied user data from social media (Facebook) whose shared posts are positive, and also the negative side posts that may lead to negative affect personally or can be further extended to the community and...
With the rapidly growing of real-time social media, like Twitter, many users share and discuss their interest topics through such platforms. Hashtag is a type of metadata tag which allows users to annotate their topics of tweets. For research usage, for example, hashtags can help the performance of event detection by observing the trend of hashtags. Although Twitter grows rapidly, hashtag growth is...
Revealing underlying social influence among users in social media is critical to understanding how users interact, on which a lot of security intelligence applications can be built. Existing methods fail to take into account the interaction relationships among memes. In this paper, we propose to simultaneously model social influence and meme interaction in information diffusion with novel multidimensional...
Microblog Sentiment Analysis (MSA) is a popular and important theme in social networks. Microblog platform such as Twitter, can collect rich microblogging messages everyday. However, for MSA tasks, it is still difficult and costly to collect sufficient manual sentiment labels for training. There are rich unlabeled microblogging messages, but only a few manual labeled messages. In this paper, we propose...
The current Analytics tools and models that are available in the market are very costly, unable to handle Big Data and less secure. The traditional Analytics systems takes a long time to come up with results, so it is not beneficial to use for Real Time Analytics. So, the proposed work resolves all these problems by combining the Apache Open Source platform which solves the issues of Real Time Analytics...
Surveillance of epidemic outbreaks and spread from social media is an important tool for governments and public health authorities. Machine learning techniques for now casting the flu have made significant inroads into correlating social media trends to case counts and prevalence of epidemics in a population. There is a disconnect between data-driven methods for forecasting flu incidence and epidemiological...
Teens who have viewed alcohol-related content on social networking sites are more likely to have consumed alcohol than teens that have not seen such content. This suggests a rising concern about the influence of these sites on adolescent drinking behavior. Parents, health organizations, and school administrators need a deeper understanding of online promotional patterns in order to combat risky behaviors...
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