Our scheme algorithmically estimates the importance of each advertisement by considering both explicit interactions between advertisements and social media users and users' popularity.
They found that followers number represents a user's popularity, but is not related to other important relations, such as retweets and mentions.
And just like high school, the interactions also play into a user's popularity level, which runs the gamut from "Trendsetter" to "VIP" according to Lee.
We demonstrate that the proposed scheme can not only protect the users' authentic popularity privacy, but also keep the full utility of the social network.
However, by systematically probing the recommender system, a large amount of correlated recommendation results can be obtained and combined, and valuable aggregated knowledge, such as system-wide cross-user item popularity ranking and item clustering, can be accurately inferred.
Therefore, it is a challenge to develop an effective anonymization algorithm to protect the privacy of user's authentic popularity in online social networks without decreasing their utility.
Our comparative study confirms that system performance is impacted by a variety of factors such as wireless access methods, mobility, user location, and popularity of the information.
That said, it's unclear to what extent live video can help boost Tumblr's popularity, user engagement, and ultimately ad dollars, as it will now be one of many live video options on the today's web.
For example, there have been many studies toestablish the reasons behind user or item popularity in social networks (e.g., [2, 3]), but the effects that the patterns of attention received have on theactivity and the engagement of the "average" users have not been thoroughlyexplored so far.
