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Social networking sites (SNS), such as Facebook and Twitter, are important spaces for political engagement. SNS have become common elements in political participation, campaigns, and elections. However, little is known about the dynamics between candidate posts and commentator sentiment in response to those posts on SNS. This study enriches computational political science by studying the 2016 U.S...
Recently, there have been considerable efforts to use online data to investigate international migration. These efforts show that Web data are valuable for estimating migration rates and are relatively easy to obtain. However, existing studies have only investigated flows of people along migration corridors, i.e. between pairs of countries. In our work, we use data about “places lived” from millions...
This paper tackles the geospatial tag estimation problem, which is of critical importance for location-based search, retrieval, and mining applications. However, tag estimation is challenging due to massive sparsity, uncertainty in the tags actually used, as well as diversity across locations and times. Toward overcoming these challenges, we propose a community-based smoothing approach that seeks...
Social media usage has increased marginally in the last decade and it is still continuing to grow. Companies, data scientists, and researchers are trying to infer meaningful information from this vast amount of data. One of the most important target applications is to find influential people in these networks. This information can serve many purposes such as; user or content recommendation, viral...
Phone number, a unique identifier has emerged as an important Personally Identifiable Information (PII) in the last few years. Other PII like e-mail and online identity have been exploited in the past to launch phishing and spam attacks against them. The reach and security of a phone number provide a genuine advantage over e-mail or online identity, making it the most vulnerable attack vector. In...
In severe outbreaks such as Ebola, bird flu and SARS, people share news, and their thoughts and responses regarding the outbreaks on social media. Understanding how people perceive the severe outbreaks, what their responses are, and what factors affect these responses become important. In this paper, we conduct a comprehensive study of understanding and mining the spread of Ebola-related information...
Community detection is a fundamental task in social network analysis. In this paper, first we develop an endorsement filtered user connectivity network by utilizing Heider's structural balance theory and certain Twitter triad patterns. Next, we develop three Nonnegative Matrix Factorization frameworks to investigate the contributions of different types of user connectivity and content information...
Cyberbullying is a major problem affecting more than half of all American teens. Prior work has largely focused on detecting cyberbullying after the fact. In this paper, we investigate the prediction of cyberbullying incidents in Instagram, a popular media-based social network. The novelty of this work is building a predictor that can anticipate the occurrence of cyberbullying incidents before they...
This paper simulates Companies' ego networks on Twitter, meaning the companies' number and type of followers. Evident from our data, we show that followers' distribution, in our focus, is neither scale free nor random, thus common network simulations cannot be used to mimic observed data. We present novel rate equations model to capture the complex dynamics of these ego networks.
Real-world network datasets are often incomplete. Subsequently, any analysis on such networks is likely to produce skewed results. We examine the following problem: given an incomplete network, which b nodes should be probed to bring as many new nodes as possible into the observed network? For instance, consider someone who has observed a portion (say 1%) of the Twitter network. How should she use...
This paper describes a methodology approach and a tool dedicated to the exploration of the Twitter social stream by combining different contextual parameters such as time, keywords, gender or the opinion. The exploration can be made in two main modes depending on the fact that the phenomenon is either known or not. The first mode, similar to the use of Googlefight search engine, allows to compare...
Social media is the collection of different social networks containing different type of information. The information may be in the form of text, video, audio and image. Also various categories of users, various types of communities are available on social network. This research reports on the extraction of new keywords from messages posted on social media which will be helpful in the identification...
The study of a large real world network in terms of graph sample representation constitutes a very powerful and useful tool in several domains of network analysis. This is the motivation that has led the work of this paper towards the development of a new graph sampling algorithm. Previous research in this area proposed simple processes such as the classic Random Walk algorithm, Random node and Random...
Although the spiral of silence theory has been studied thoroughly in the traditional dissemination field, to our best knowledge, no one has clearly verified the applicability of the spiral of silence theory in social networks based on the real information propagation datasets. In this paper, we focus on the disparity between majority and minority opinions, we verify the applicability of the spiral...
Quad Closure is a group of four people who are connected with each other. In this paper, we propose a new group recognition method for Instagram which are based on triadic closure method to determine groups on dynamic social networks (e.g likes and comments) between users as named Quad Closure. Social networks are not easily classified because of their complexity. We study how an open quad closure...
In the current era of big data, high volumes of valuable data can be easily collected and generated. Social networks are examples of generating sources of these big data. Users (or social entities) in these social networks are often linked by some interdependency such as friendship or ‘following’ relationships. As these big social networks keep growing, there are situations in which an individual...
The thousands of streaming data overwhelmingly provide for Internet users on Twitter every day, especially for those Twitter users with many friends. However, the useful tweets that users are really interested in personally could be covered by massive other uninformative and uninteresting information. Therefore, how to bring immediately the interesting tweets for users is always a challenging issue...
Identifying and communicating relationships between causes and effects is important for understanding our world, but is affected by language structure, cognitive and emotional biases, and the properties of the communication medium. Despite the increasing importance of social media, much remains unknown about causal statements made online. To study real-world causal attribution, we extract a large-scale...
The large amount of user generated data that Online Social Networks produce has remarkably drawn the attention for researchers on human behavior in the recent years. In this work, we use temporal series and complex network analysis to unveil the users' behavioral patterns during the Spanish presidential electoral campaigns in Twitter. We introduce a new measure to study political sentiment in Twitter,...
As the spread of rumours has been increasing every day in online social networks (OSNs), it is important to analyze and understand this phenomenon. Damage caused by the spread of rumours is difficult to handle without a full understanding of the dynamics behind it. One of the central steps of understanding rumour spread is to analyze who spread rumours online, why, and how. In this research, we focus...
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