A community role approach to assess social capitalists visibility in the Twitter network

In the context of Twitter, social capitalists are specific users trying to increase their number of followers and interactions by any means. These users are not healthy for the service, because they are either spammers or real users flawing the notions of influence and visibility. Studying their behavior and understanding their position in Twit-ter is thus of important interest. It is also necessary to analyze how these methods effectively affect user visibility. Based on a recently proposed method allowing to identify social capitalists, we tackle both points by studying how they are organized, and how their links spread across the Twitter follower-followee network. To that aim, we consider their position in the network w.r.t. its community structure. We use the concept of community role of a node, which describes its position in a network depending on its connectiv-ity at the community level. However, the topological measures originally defined to characterize these roles consider only certain aspects of the community-related connectivity, and rely on a set of empirically fixed thresholds. We first show the limitations of these measures, before extending and generalizing them. Moreover, we use an unsupervised approach to identify the roles, in order to provide more flexibility relatively to the studied system. We then apply our method to the case of social capitalists and show they are highly visible on Twitter, due to the specific roles they hold.Comment: arXiv admin note: substantial text overlap with arXiv:1406.661

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Addressing the new generation of spam (Spam 2.0) through Web usage models

New Internet collaborative media introduce new ways of communicating that are not immune to abuse. A fake eye-catching profile in social networking websites, a promotional review, a response to a thread in online forums with unsolicited content or a manipulated Wiki page, are examples of new the generation of spam on the web, referred to as Web 2.0 Spam or Spam 2.0. Spam 2.0 is defined as the propagation of unsolicited, anonymous, mass content to infiltrate legitimate Web 2.0 applications.The current literature does not address Spam 2.0 in depth and the outcome of efforts to date are inadequate. The aim of this research is to formalise a definition for Spam 2.0 and provide Spam 2.0 filtering solutions. Early-detection, extendibility, robustness and adaptability are key factors in the design of the proposed method.This dissertation provides a comprehensive survey of the state-of-the-art web spam and Spam 2.0 filtering methods to highlight the unresolved issues and open problems, while at the same time effectively capturing the knowledge in the domain of spam filtering.This dissertation proposes three solutions in the area of Spam 2.0 filtering including: (1) characterising and profiling Spam 2.0, (2) Early-Detection based Spam 2.0 Filtering (EDSF) approach, and (3) On-the-Fly Spam 2.0 Filtering (OFSF) approach. All the proposed solutions are tested against real-world datasets and their performance is compared with that of existing Spam 2.0 filtering methods.This work has coined the term ‘Spam 2.0’, provided insight into the nature of Spam 2.0, and proposed filtering mechanisms to address this new and rapidly evolving problem

Multilevel User Credibility Assessment in Social Networks

Online social networks are one of the largest platforms for disseminating both real and fake news. Many users on these networks, intentionally or unintentionally, spread harmful content, fake news, and rumors in fields such as politics and business. As a result, numerous studies have been conducted in recent years to assess the credibility of users. A shortcoming of most of existing methods is that they assess users by placing them in one of two categories, real or fake. However, in real-world applications it is usually more desirable to consider several levels of user credibility. Another shortcoming is that existing approaches only use a portion of important features, which downgrades their performance. In this paper, due to the lack of an appropriate dataset for multilevel user credibility assessment, first we design a method to collect data suitable to assess credibility at multiple levels. Then, we develop the MultiCred model that places users at one of several levels of credibility, based on a rich and diverse set of features extracted from users' profile, tweets and comments. MultiCred exploits deep language models to analyze textual data and deep neural models to process non-textual features. Our extensive experiments reveal that MultiCred considerably outperforms existing approaches, in terms of several accuracy measures

Vulnerabilities to Online Social Network Identity Deception Detection Research and Recommendations for Mitigation

Identity deception in online social networks is a pervasive problem. Ongoing research is developing methods for identity deception detection. However, the real-world efficacy of these methods is currently unknown because they have been evaluated largely through laboratory experiments. We present a review of representative state-of-the-art results on identity deception detection. Based on this analysis, we identify common methodological weaknesses for these approaches, and we propose recommendations that can increase their effectiveness for when they are applied in real-world environments

Trustworthiness in Social Big Data Incorporating Semantic Analysis, Machine Learning and Distributed Data Processing

This thesis presents several state-of-the-art approaches constructed for the purpose of (i) studying the trustworthiness of users in Online Social Network platforms, (ii) deriving concealed knowledge from their textual content, and (iii) classifying and predicting the domain knowledge of users and their content. The developed approaches are refined through proof-of-concept experiments, several benchmark comparisons, and appropriate and rigorous evaluation metrics to verify and validate their effectiveness and efficiency, and hence, those of the applied frameworks

Sampling Twitter users for social science research: Evidence from a systematic review of the literature

All social media platforms can be used to conduct social science research, but Twitter is the most popular as it provides its data via several Application Programming Interfaces, which allows qualitative and quantitative research to be conducted with its members. As Twitter is a huge universe, both in number of users and amount of data, sampling is generally required when using it for research purposes. Researchers only recently began to question whether tweet-level sampling—in which the tweet is the sampling unit—should be replaced by user-level sampling—in which the user is the sampling unit. The major rationale for this shift is that tweet-level sampling does not consider the fact that some core discussants on Twitter are much more active tweeters than other less active users, thus causing a sample biased towards the more active users. The knowledge on how to select representative samples of users in the Twitterverse is still insufficient despite its relevance for reliable and valid research outcomes. This paper contributes to this topic by presenting a systematic quantitative literature review of sampling plans designed and executed in the context of social science research in Twitter, including: (1) the definition of the target populations, (2) the sampling frames used to support sample selection, (3) the sampling methods used to obtain samples of Twitter users, (4) how data is collected from Twitter users, (5) the size of the samples, and (6) how research validity is addressed. This review can be a methodological guide for professionals and academics who want to conduct social science research involving Twitter users and the Twitterverse.info:eu-repo/semantics/publishedVersio