Information In Online Social Networks Research Articles

Lifespan and propagation of information in On-line Social Networks: A case study based on Reddit

Since 1950, information flows have been in the center of scientific research. Up until the Internet penetration in the late 1990s, these information flow studies were based on traditional offline social networks. From the first Online Social Network studies, various observations of “offline” information flows, such as the two-step flow of communication and the importance of weak ties, were verified in several “online” studies, also indicating that information flows from one Online Social Network (OSN) to several others. Within that flow, information is shared with and reproduced, by users of each network. Furthermore, the original content is enhanced or weakened according to its topic, as well as the dynamic nature and exposure of each Online Social Networks (OSNs). In such an informational connected environment, each OSN is considered as a layer of information flows, which interacts with other layers. We examine information flows in several social networks, as well as their diffusion and lifespan, across these networks, based on user-generated content. Our results verify the information connection in various OSNs and provide a measurement of shared information lifetime in multiple OSNs.

A new access control scheme for Facebook-style social networks

The popularity of online social networks (OSNs) makes the protection of users’ private information an important but scientifically challenging problem. In the literature, relationship-based access control schemes have been proposed to address this problem. However, with the dynamic developments of OSNs, we identify new access control requirements which cannot be fully captured by the current schemes. In this paper, we focus on public information in OSNs and treat it as a new dimension which users can use to regulate access to their resources. We define a new OSN model containing users and their relationships as well as public information. Based on this model, we introduce a variant of hybrid logic for formulating access control policies. We exploit a type of category information and relationship hierarchy to further extend our logic for its usage in practice. In the end, we propose a few solutions to address the problem of information reliability in OSNs, and formally model collaborative access control in our access control scheme.

Trend Dynamics and Attention in Chinese Social Media

We analyzed the temporal aspect of trends and trend-setters in Sina Weibo, contrasting it with earlier observations of Twitter. We found a vast difference in the content shared in China as compared with a global social network such as Twitter. In China, the trends are created almost entirely due to the retweets of media content such as jokes, images, and videos, unlike Twitter where trends have more to do with current global events and news stories. On closer inspection, we observed that most trends in Sina Weibo are due to the continuous retweets of a small percentage of fraudulent accounts, set up to artificially inflate certain posts. This reveals evidence of an “Internet Water Army,” a unique promotional method deployed by public relations companies in China to influence the popular dissemination of information in online social networks.

A Sword with Two Edges: Propagation Studies on Both Positive and Negative Information in Online Social Networks

Online social networks (OSN) have become one of the major platforms for people to exchange information. Both positive information (e.g., ideas, news and opinions) and negative information (e.g., rumors and gossips) spreading in social media can greatly influence our lives. Previously, researchers have proposed models to understand their propagation dynamics. However, those were merely simulations in nature and only focused on the spread of one type of information. Due to the human-related factors involved, simultaneous spread of negative and positive information cannot be thought of the superposition of two independent propagations. In order to fix these deficiencies, we propose an analytical model which is built stochastically from a node level up. It can present the temporal dynamics of spread such as the time people check newly arrived messages or forward them. Moreover, it is capable of capturing people’s behavioral differences in preferring what to believe or disbelieve. We studied the social parameters impact on propagation using this model. We found that some factors such as people’s preference and the injection time of the opposing information are critical to the propagation but some others such as the hearsay forwarding intention have little impact on it. The extensive simulations conducted on the real topologies confirm the high accuracy of our model.

IDENTIFYING RELIABLE POSTS AND USERS IN ONLINE SOCIAL NETWORKS

In the age of Web 2.0, generalizing concepts are mainly based on information sharing and user cooperation. The user-centric Internet mode encourages more users to participate in the Internet. However, this mode causes the flooding of social networks with low-quality information. Thus, identifying trusted information in online social networks (OSNs) so as to enable the Internet to serve humans better has gradually become a research hotspot. However, most studies evaluate the quality of user-created content or discriminate reliable users in isolation. These results are specific to particular social networks and lack generality. Intuitively, reliable content and users are closely related. In this study, the authors attempt to combine these two types of reliable data by separately identifying reliable posts and users in a social network. Posts and users are found to improve each other based on the preliminary identification results. To deal with imbalanced data, an algorithm that combines oversampling and undersampling is used to build balanced data. An ensemble classifier is adopted for data classification. Experiments show that the proposed framework is both effective and efficient for most types of OSNs. The contributions of this study are two-fold: (i) a framework combining user-created content and reliable user recognition is proposed, and (ii) an ensemble classifier is built for use in data classification.

The emergence and impact of consumer brand empowerment in online social networks: A proposed ontology

This article presents a proposed model introducing consumer brand empowerment and discussing its antecedents and outcomes. Consumer brand empowerment reflects the perceived empowerment resulting from the consumer’s influence over the brand-related attitudes and behaviors of other consumers in an online social network. The antecedents in the proposed ontology focus on the sharing of brand information in online social networks and how it promotes a sense of consumer empowerment by enabling a consumer to influence the brand attitudes of other consumers. The online social network participation factors studied include reach and how its impact is moderated by variety, depth and frequency of brand-related information sharing in social networks. The outcomes resulting from consumer brand empowerment relate to the desired cognitive, affective and behavioral brand experiences sought by an empowered consumer. The article discusses the implications of the research relative to both marketing researchers and managers interested in understanding brand management within the emerging social network landscape.

Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack

Little is currently known about the factors that promote the propagation of information in online social networks following terrorist events. In this paper we took the case of the terrorist event in Woolwich, London in 2013 and built models to predict information flow size and survival using data derived from the popular social networking site Twitter. We define information flows as the propagation over time of information posted to Twitter via the action of retweeting. Following a comparison with different predictive methods, and due to the distribution exhibited by our dependent size measure, we used the zero-truncated negative binomial (ZTNB) regression method. To model survival, the Cox regression technique was used because it estimates proportional hazard rates for independent measures. Following a principal component analysis to reduce the dimensionality of the data, social, temporal and content factors of the tweet were used as predictors in both models. Given the likely emotive reaction caused by the event, we emphasize the influence of emotive content on propagation in the discussion section. From a sample of Twitter data collected following the event (N = 427,330) we report novel findings that identify that the sentiment expressed in the tweet is statistically significantly predictive of both size and survival of information flows of this nature. Furthermore, the number of offline press reports relating to the event published on the day the tweet was posted was a significant predictor of size, as was the tension expressed in a tweet in relation to survival. Furthermore, time lags between retweets and the co-occurrence of URLS and hashtags also emerged as significant.

Viral spreading of daily information in online social networks

We explain a possible mechanism of an information spreading on a network which spreads extremely far from a seed node, namely the viral spreading. On the basis of a model of the information spreading in an online social network, in which the dynamics is expressed as a random multiplicative process of the spreading rates, we will show that the correlation between the spreading rates enhances the chance of the viral spreading, shifting the tipping point at which the spreading goes viral.

Energy saving practice diffusion in online networks

Abstract Efficient diffusion of information regarding energy conservation is imperative for the widespread adoption of sustainable behavior. Although some researchers have argued that this phenomenon can unfold similarly to an epidemic, especially in online communication networks, others have found that information disseminates within a narrow range, propagating only a few levels deep in a social network. To address this issue, 108,771 energy saving practice tweets were collected from Twitter and then used to develop an information cascade model. The model was then validated by comparing simulation results with empirical data. A variance-based global sensitivity analysis determined the impact of two network attributes—network density and the number of an initiator's connections—to four other factors on information cascading. The empirical data showed that energy saving information failed to generate deep cascades and the simulation results demonstrated that the network attributes, network density and the number of an initiator's connections had limited influence on information cascades unless combined with other factors. These findings suggest that massive network structures and a large number of potential recipients cannot guarantee deep cascades of energy saving information in online social networks.

Using community structure to control information sharing in online social networks

The dominant role of social networking in the web is turning human relations into conduits of information flow. This means that the way information spreads on the web is determined to a large extent by human decisions. Consequently, information security lies on the quality of the collective decisions made by the users. Recently, many access control schemes have been proposed to control unauthorized propagation of information in online social networks; however, there is still a need for mechanisms to evaluate the risk of information leakage within social networks. In this paper, we present a novel community-centric confidentiality control mechanism for information flow management on the web. We use a Monte Carlo based algorithm to determine the potential spread of a shared data object and to inform the user of the risk of information leakage associated with different sharing decisions she can make in a social network. By using the information provided by our algorithm, the user can curtail sharing decisions to reduce the risk of information leakage. Alternatively, our algorithm can provide input for a fully- or semi-automatic sharing decision maker that will determine the outcomes of sharing requests. Our scheme also provides a facility to reduce information flowing to a specific user (i.e., black listing a specific user). We used datasets from Facebook and Flickr to evaluate the performance of the proposed algorithms under different sharing conditions. The simulation results indicate that our algorithm can effectively control information sharing to reduce the risk of information leakage.

The Study of Information Dissemination Behavior in Online Social Network with Propagation Delay

In this paper, the information dissemination model involving propagation delay was studied. The effectof delay in the Social Network Service (SNS) was found by considering the theory of complex network and in fectious diseasedynamics. Also, the authors simulate the online social network information dissemination process and classify thedifferent types of nodes. The simulation results show that: as the high connectivity of online social network, there is less threshold of information transmission in network; the propagation delay makes the threshold lower and the information is easier to be spread, but the propagation delay speeds down the information dissemination meanwhile it decreases the possibility of the information out breaking. This study posts a well understanding of the dissemination behavior with propagation delay in the SNS network, which is fundamental research on consensus.

Analysis and modelling of semantic attacks in online social networks

The emergence of online social networks as an important media for communication and information dissemination during the last decade has also seen the increase in abuse of the media to spread misinformation, disinformation and propaganda. Detecting different types of semantic attacks possible in online social networks would require their accurate classification. Drawing similarities with other social computing systems like recommender systems, we propose a new taxonomy for semantic attacks in online social networks. We propose an algorithm which uses social network as a medium of social computing to analyse patterns of propagation of information and identify sources of disinformation in them. We construct a new information repropagation graph from social network data and carry out iterative core analysis of the graph to isolate possible contents of misinformation and user nodes which are involved in their propagation. We used seven different datasets obtained from Twitter to validate our results. We also propose an information propagation model for deliberate spread of false information in online social networks.

Predicting Audience Location on the Basis of thek-Nearest Neighbor Multilabel Classification

Understanding audience location information in online social networks is important in designing recommendation systems, improving information dissemination, and so on. In this paper, we focus on predicting the location distribution of audiences on YouTube. And we transform this problem to a multilabel classification problem, while we find there exist three problems when the classicalk-nearest neighbor based algorithm for multilabel classification (ML-kNN) is used to predict location distribution. Firstly, the feature weights are not considered in measuring the similarity degree. Secondly, it consumes considerable computing time in finding similar items by traversing all the training set. Thirdly, the goal of ML-kNN is to find relevant labels for every sample which is different from audience location prediction. To solve these problems, we propose the methods of measuring similarity based on weight, quickly finding similar items, and ranking a specific number of labels. On the basis of these methods and the ML-kNN, thek-nearest neighbor based model for audience location prediction (AL-kNN) is proposed for predicting audience location. The experiments based on massive YouTube data show that the proposed model can more accurately predict the location of YouTube video audience than the ML-kNN, MLNB, and Rank-SVM methods.

Exploring the Use of Egocentric Online Social Network Data to Characterize Individual Air Travel Behavior

The rapid growth of online social networking over the past decade has generated substantial amounts of data about individuals and their social relationships. Recent research studies investigating social relationships and travel behavior have sought connections between individuals’ social networks and socially related travel; however, to the authors’ knowledge, no study has pursued the use of online social networking data to do so. This study explores the use of online social network data in characterizing individuals’ air travel behavior. Data were collected through a web-based survey including information about individuals’ air travel history and online social network information, specifically participants’ Facebook networks. The data were then analyzed to address a series of hypotheses about the association between online social network characteristics (specifically Facebook) and air travel behavior, in particular, travel distance, leisure-related travel, and trip generation. This study found a positive relationship between the size and distribution of individuals’ Facebook social networks and both their engagement in air travel and the odds that their air travel will be leisure related or will include a leisure component.

On Charactering of Information Propagation in Online Social Networks

Online social networks (OSNs) are becoming an important propagation platform for Word of mouth (WOM). Therefore, it is of great significance to study the propagation of WOM in OSNs. A WOM propagation model named N-P-N is proposed in this paper, and some simulation experiments are carried out to investigate the mechanism of WOM propagation. From the sensitivity analysis of degree of initial information source node, it can be seen that the degree of initial information source node determines the scope and speed of the propagation of WOM in OSNs in some extent. Then the sensitivity analysis of number of initial information source nodes shows that the initial source nodes are crucial for controlling the propagation of negative information in OSNs. Moreover, from the user behavior respect, it is found that different user behavior in OSNs causes different propagation results, the more users who are willing to diffuse WOM, the more scope WOM can propagate and the faster the information diffuses. Findings in this paper are helpful for enterprises to form an effective WOM.

Word of Mouth Propagation in Online Social Networks

Online social networks (OSNs) are becoming an important propagation platform for Word of mouth (WOM). Therefore, it is of great significance to study the propagation of WOM in OSNs. A WOM propagation model named N-P-N is proposed in this paper, and some simulation experiments are carried out to investigate the mechanism of WOM propagation. From the sensitivity analysis of degree of initial information source node, it can be seen that the degree of initial information source node determines the scope and speed of the propagation of WOM in OSNs in some extent. Then the sensitivity analysis of number of initial information source nodes shows that the initial source nodes are crucial for controlling the propagation of negative information in OSNs. Moreover, from the user behavior respect, it is found that different user behavior in OSNs causes different propagation results, the more users who are willing to diffuse WOM, the more scope WOM can propagate and the faster the information diffuses. Findings in this paper are helpful for enterprises to form an effective WOM.

Quantifying virality of information in Online Social Networks

Quantifying virality of information in Online Social Networks

Quantifying Virality of Information in Online Social Networks

The aim of this research is to propose a model through which the viral nature of an information item in an online social network can be quantified. Further, the authors propose an alternate technique for information asset valuation by accommodating virality in it which not only complements the existing valuation system, but also improves the accuracy of the results. They use a popularly available YouTube dataset to collect attributes and measure critical factors such as share-count, appreciation, user rating, controversiality, and comment rate. These variables are used with a proposed formula to obtain viral index of each video on a given date. The authors then identify a conventional and a hybrid asset valuation technique to demonstrate how virality can fit in to provide accurate results.The research demonstrates the dependency of virality on critical social network factors. With the help of a second dataset acquired, the authors determine the pattern virality of an information item takes over time.

Privacy as information access and illusory control: The case of the Facebook News Feed privacy outcry

Increasingly, millions of people, especially youth, post personal information in online social networks (OSNs). In September 2006, one of the most popular sites—Facebook.com—introduced the features of News Feed and Mini Feed, revealing no more information than before, but resulting in immediate criticism from users. To investigate the privacy controversy, we conducted a survey among 172 current Facebook users in a large US university to explore their usage behaviors and privacy attitudes toward the introduction of the controversial News Feed and Mini Feed features. We examined the degree to which users were upset by the changes, explored the reasons as to why, and examined the influences of the News Feed privacy outcry on user behavior changes. The results have demonstrated how an easier information access and an “illusory” loss of control prompted by the introduction of News Feed features, triggered users’ privacy concerns. In addition to enhancing our theoretical understanding of privacy issues in the online social networks, this research is also potentially useful to privacy advocates, regulatory bodies, service providers, and marketers to help shape or justify their decisions concerning the online social networks.