Formation and maintenance of social media accounts and their relationships with other accounts are associated with various social outcomes.[12] In 2015, for many firms, customer relationship management is essential and is partially done through Facebook.[13] Before the emergence and prevalence of social media, customer identification was primarily based upon information that a firm could directly acquire:[14] for example, it may be through a customer's purchasing process or voluntary act of completing a survey/loyalty program. However, the rise of social media has greatly reduced the approach of building a customer's profile/model based on available data. Marketers now increasingly seek customer information through Facebook;[13] this may include a variety of information users disclose to all users or partial users on Facebook: name, gender, date of birth, e-mail address, sexual orientation, marital status, interests, hobbies, favorite sports team(s), favorite athlete(s), or favorite music, and more importantly, Facebook connections.[13]
However, due to the privacy policy design, acquiring true information on Facebook is no trivial task. Often, Facebook users either refuse to disclose true information (sometimes using pseudonyms) or setting information to be only visible to friends, Facebook users who "LIKE" your page are also hard to identify. To do online profiling of users and cluster users, marketers and companies can and will access the following kinds of data: gender, the IP address and city of each user through the Facebook Insight page, who "LIKED" a certain user, a page list of all the pages that a person "LIKED" (transaction data), other people that a user follow (even if it exceeds the first 500, which we usually can not see) and all the publicly shared data.[13]
First Zooomr Survey
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Recently, more and more research efforts have been dedicated to the aforementioned challenges and opportunities. Especially in the past three years, related papers are extensively published in ACM MM, SIGIR, WWW, and CVPR. Yet, a special issue focusing on this specific topic on related journals is still missing so far. Therefore, we see a timely opportunity to organize a special issue to bring together active researchers to share recent progress in this exciting area. Our goals are three-fold: (1) theories and applications on social multimedia computing related to location services; (2) survey on the progress of this area in the past years; (3) discuss emerging applications based on this newly scenarios.
Visual search retains as one of the most important topics in social media analytics. In this special issue, we first introduce the work from Xia et al. (Geometric discriminative features for aerial image retrieval in social media) that adopts geometric discriminative features for aerial image retrieval in social media. Then, we introduce the work from Cheng et al. (The effects of multiple query evidences on social image retrieval) to analyze the effects of multiple query evidences on social image retrieval. Both papers give systematic analysis on how the image search problem is challenging and where is the potential solution to improve image search system in the context of social media data. In Li et al. (Fast verification via statistical geometric for mobile visual search) a statistical scheme is presented to replace RANSAC in scenario where fast spatial verification is needed. 2ff7e9595c
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