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A content analysis of online sex worker advertisements suggests specific terms, sources and patterns of behavior that may help identify potential sex trafficked victims within these virtual environments. While some ads are posted by independent sex workers, others may have been posted by traffickers or pimps, advertising the women they have under their control. A total of 600 ads from the Backpage...
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...
Stored data in database can hide some knowledge, which is interesting, useful to hidden knowledge discover. In this context, an algorithms number a frequent itemsets and association rules extraction were presented. Special feature of these algorithms is to generation a large number of rules, making their exploitation a difficult task. In this paper we will introduce a new algorithm for association...
Companies today are developing business strategies taking into consideration behavior of their customers through social networks, which have allowed to extract large amounts of relevant data about users. This is why it has been necessary to apply data mining techniques to find patterns that describe the preferences of users in different contexts. This paper describes the results of using data mining...
We design a way to model apps as vectors, inspired by the recent deep learning approach to vectorization of words called word2vec. Our method relies on how users use apps. In particular, we visualize the time series of how each user uses mobile apps as a “document”, and apply the recent word2vec modeling on these documents, but the novelty is that the training context is carefully weighted by the...
The increase in analysis of real life social networks has led to a better understanding of the ways humans socialize in a group. Since trust is an important part of any social interaction, researchers use such networks to understand the nuances of trust relationships. One of the major requirements in trust applications is identifying the trustworthy actors in these networks. This paper proposes a...
Many recent studies on finance and social networks discovered that investor's attention is correlated to the financial market movement in terms of the price shocks. Following related findings, a significant and challenging problem is to forecast the direction of the market movement based on vast social media activities. Appropriately processing social networks data and developing models to capture...
Cyberbullying is the most common online risk for adolescents, and it has been reported that over half of young people do not tell their parents when it occurs. Cyberbullying involves the deliberate use of online digital media to communicate false or embarrassing information about another person. While previous work has extensively analyzed the nature and prevalence of cyberbullying, there has been...
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...
To incentivize users' participations, online social networks often provide users with various rewards for their contributions to the sites. Attracted by the rewards, users will spend more time using the network services. Specifically, in this paper, we will mainly focus on “badges reward systems”. Badges are small icons attached to users' homepages and profiles denoting their achievements. People...
Individuals have distinctive ways of speaking and writing, and there exists a long history of linguistic and stylistic investigation into authorship attribution. Most authorship identification approaches are exclusively based on lexical measures such as vocabulary richness and lexico-syntactic features, or substantially generate relevant features for different machine learning approaches. These techniques...
Efficient organization and analysis of academic information has many advantages. Most scholar retrieval systems appeared these years can perform keyword-based paper search. However, performing large-scale expert and paper retrieval is an intractable problem. Here we present a platform that can not only reduce the workload of researchers when searching academic literature, but also promote academic...
We study a natural problem: Given a small piece of a large parent network, is it possible to identify the parent network? We approach this problem from two perspectives. First, using several “sophisticated” or “classical” network features that have been developed over decades of social network study. These features measure aggregate properties of the network and have been found to take on distinctive...
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...
Dynamic community detection has been of great significance on analyzing network structure and community evolution. Among state-of-the-art methods, incremental algorithms based on modularity have been used widely, for the fully utilization of both current and historical information. Unfortunately, they are difficult to uncover small community due to problem called “resolution limit” and also sensitive...
In community question and answering sites, pairs of questions and their high-quality answers (like best answers selected by askers) can be valuable knowledge available to others. However lots of questions receive multiple answers but askers do not label either one as the accepted or best one even when some replies answer their questions. To solve this problem, high-quality answer prediction or best...
A central problem in analyzing networks is partitioning them into modules or communities. One of the best tools for this is the stochastic block model, which clusters vertices into blocks with statistically homogeneous pattern of links. Despite its flexibility and popularity, there has been a lack of principled statistical model selection criteria for the stochastic block model. Here we propose a...
Authenticity is the key for online review sites. Due to the significant development of review sites, the reviews are now highly important to users, producers and other stakeholders. Driven by interest, some imposters begin to post fake reviews to promote or discredit target products. The fake reviews not only mislead the users but also damage the service provider's credit. Current works mostly aim...
This paper introduces two models for influence in networks, and presents some upper and lower bounds for time needed to reach stability in these models. The first, called the Majority Model, is an expansion on the “Democrats and Republicans Model” that uses cascades to initialize the influence network rather than randomly assigning each node an initial opinion. By slightly modifying a network introduced...
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