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Users of online social networks often adjust their privacy settings to control how much information on their profiles is accessible to other users of the networks. While a variety of factors have been shown to affect the privacy strategies of these users, very little work has been done in analyzing how these factors influence each other and collect...
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... consider different levels of the risk, we choose 3 different benefit-to-risk ratios (BRRs), which are 1 : 0, 1 : 15, and 1 : 30 (cf. Table 2). While all the attributes are assigned to different weights, the weight vector for the attributes is assumed to be the same for each user of the network. ...Context 2
... all the attributes are assigned to different weights, the weight vector for the attributes is assumed to be the same for each user of the network. Additional simulation settings are shown in Table 2. We run the simulation for each configuration 500 times. ...Context 3
... consider different levels of the risk, we choose 3 different benefit-to-risk ratios (BRRs), which are 1 : 0, 1 : 15, and 1 : 30 (cf. Table 2). While all the attributes are assigned to different weights, the weight vector for the attributes is assumed to be the same for each user of the network. ...Citations
... We have utilized game theory to design models for privacy settings of online social networks in our previous works [19] [20]. As future work, we plan to integrate the sentiment factor which has been studied in this paper into a game theoretic model to better predict the dynamics of retweeting. ...
Retweeting is an important way of information propagation on Twitter. In this paper, we investigate the sentiment correlation between regular tweets and retweets. We anticipate our investigation sheds a light on how the sentiment of regular tweets impacts the retweets of different sentiments. We propose a method for measuring the sentiment of tweets. We categorize the Twitter users into different groups by different norms, which are the follower count, the betweenness connectivity, a combination of follower count and betweenness centrality, and the amount of tweets. Then, we calculate the sentiment correlation for different groups to examine the influential factors for retweeting a message with a certain sentiment. We find that the users with higher betweenness centrality and higher tweets amount tend to exhibit a higher sentiment correlation. The users with medium-level show the highest sentiment correlation compared to the low-level and high-level . After combining the two factors of and betweenness centrality, we discover that specifically at low-level betweenness centrality the users with medium-level have the highest sentiment correlation. Our last observation is that the difference for correlation coefficients exists between different types of users. Our study on the sentiment correlation provides instructional information for modeling information propagation in human society.
... Game theory has been applied to model the influence from the interactions between OSN users on the privacy settings. Chen et al. modeled privacy setting of online social networks by a two-player game and an evolutionary game, and investigated the effect of network connectivity and attribute importance on the users' profile disclosure [8] [9]. ...
Twitter users often crave more followers to increase their social popularity. While a variety of factors have been shown to attract the followers, very little work has been done to analyze the mechanism how Twitter users follow or unfollow each other. In this paper, we apply game theory to modeling the follow-unfollow mechanism on Twitter. We first present a two-player game which is based on the Prisoner's Dilemma, and subsequently evaluate the payoffs when the two players adopt different strategies. To allow two players to play multiple rounds of the game, we propose a multi-stage game model. We design a Twitter bot analyzer which follows or unfollows other Twitter users by adopting the strategies from the multi-stage game. We develop an algorithm which enables the Twitter bot analyzer to automatically collect and analyze the data. The results from analyzing the data collected in our experiment show that the follow-back ratios for both of the Twitter bots are very low, which are 0.76% and 0.86%. This means that most of the Twitter users do not cooperate and only want to be followed instead of following others. Our results also exhibit the effect of different strategies on the follow-back followers and on the non-following followers as well.
... We have utilized game theory to design models for privacy settings of online social networks in our previous works [19] [20]. As future work, we plan to integrate the sentiment factor which has been studied in this paper into a game theoretic model to better predict the dynamics of retweeting. ...
In this paper, we study the influence from the sentiment of regular tweets on retweeting. We propose a method to calculate the sentiment score for each tweet and each Twitter user. This method enables us to place the tweets and retweets into the same time period to explore the sentiment factor. We adopt the correlation coefficient between the sentiment scores of regular tweets and those of retweets to measure the influence. We categorize the Twitter users in three different ways to investigate three factors, which are the number of followers, betweenness centrality and the types of accounts. Community detection and machine learning are integrated into our approach. We find that the difference for correlation coefficients exists between different levels of the number of followers, and different types of users. Our method sheds a light on better predicting the dynamics of tweets diffusion by including the sentiment factor into the prediction model.
... In previous works [16,23], we apply a weighted evolutionary game to model privacy settings of online social networks. This model captures the relative importance of profile attributes by assigning different weights to different attributes. ...
Privacy settings are a crucial part of any online social network as users are confronted with determining which and how many profile attributes to disclose. Revealing more attributes increases users’ chances of finding friends and yet leaves users more vulnerable to dangers such as identity theft. In this paper, we consider the problem of finding the optimal strategy for the disclosure of user attributes in social networks from a game-theoretic perspective.
We model the privacy settings’ dynamics of social networks with three game-theoretic approaches. In a two-user game, each user selects an ideal number of attributes to disclose to each other according to a utility function. We extend this model with a basic evolutionary game to observe how much of their profiles users are comfortable with revealing, and how this changes over time. We then consider a weighted evolutionary game to investigate the influence of attribute importance and the network topology in selecting privacy settings.
The two-user game results show how one user’s privacy settings are influenced by the settings of another user. The basic evolutionary game results show that the higher the motivation to reveal attributes, the longer users take to stabilize their privacy settings. Results from the weighted evolutionary game show that users are more likely to reveal their most important attributes than their least important attributes regardless of the risk. Results also show that the network topology has a considerable effect on the privacy in a risk-included environment but limited effect in a risk-free environment.
Motivation and risk are identified as important factors in determining how efficiently stability of privacy settings is achieved and what settings users will adopt given different parameters. Additionally, the privacy settings are affected by the network topology and the importance users attach to specific attributes. Our models indicate that users of social networks eventually adopt profile settings that provide the highest possible privacy if there is any risk, despite how high the motivation to reveal attributes is. The provided models and the gained results are particularly important to social network designers and providers because they enable us to understand the influence of different factors on users’ privacy choices.
... In previous works [16,23], we apply a weighted evolutionary game to model privacy settings of online social networks. This model captures the relative importance of profile attributes by assigning different weights to different attributes. ...
Privacy settings are a crucial part of any online social network as users are confronted with determining which and how many profile attributes to disclose. Revealing more attributes increases users' chances of finding friends and yet leaves users more vulnerable to dangers such as identity theft. In this paper, we consider the problem of finding the optimal strategy for the disclosure of user attributes in social networks from a game-theoretic perspective. We model the privacy settings' dynamics of social networks with three game-theoretic approaches. In a two-user game, each user selects an ideal number of attributes to disclose to each other according to a utility function. We extend this model with a basic evolutionary game to observe how much of their profiles users are comfortable with revealing, and how this changes over time. We then consider a weighted evolutionary game to investigate the influence of attribute importance and the network topology in selecting privacy settings. The two-user game results show how one user's privacy settings are influenced by the settings of another user. The basic evolutionary game results show that the higher the motivation to reveal attributes, the longer users take to stabilize their privacy settings. Results from the weighted evolutionary game show that users are more likely to reveal their most important attributes than their least important attributes regardless of the risk. Results also show that the network topology has a considerable effect on the privacy in a risk-included environment but limited effect in a risk-free environment. Motivation and risk are identified as important factors in determining how efficiently stability of privacy settings is achieved and what settings users will adopt given different parameters. Additionally, the privacy settings are affected by the network topology and the importance users attach to specific attributes. Our models indicate that users of social networks eventually adopt profile settings that provide the highest possible privacy if there is any risk, despite how high the motivation to reveal attributes is. The provided models and the gained results are particularly important to social network designers and providers because they enable us to understand the influence of different factors on users' privacy choices.
Mobile location-based services (LBSs) empowered by mobile crowdsourcing provide users with context-aware intelligent services based on user locations. As smartphones are capable of collecting and disseminating massive user location-embedded sensing information, privacy preservation for mobile users has become a crucial issue. This paper proposes a metric called privacy exposure to quantify the notion of privacy, which is subjective and qualitative in nature, in order to support mobile LBSs to evaluate the effectiveness of privacy-preserving solutions. This metric incorporates activity coverage and activity uniformity to address two primary privacy threats, namely activity hotspot disclosure and activity transition disclosure. In addition, we propose an algorithm to minimize privacy exposure for mobile LBSs. We evaluate the proposed metric and the privacy-preserving sensing algorithm via extensive simulations. Moreover, we have also implemented the algorithm in an Android-based mobile system and conducted real-world experiments. Both our simulations and experimental results demonstrate that (1) the proposed metric can properly quantify the privacy exposure level of human activities in the spatial domain and (2) the proposed algorithm can effectively cloak users' activity hotspots and transitions at both high and low user-mobility levels.
