You are what emojis say about your pictures: Language - independent gender inference attack on Facebook

International audienceThe picture owner's gender has a strong influence on individuals' emotional reactions to the picture. In this study, we investigate gender inference attacks on their owners from pictures meta-data composed of: (i) alt-texts generated by Facebook to describe the content of pictures, and (ii) Emojis/Emoticons posted by friends, friends of friends or regular users as a reaction to the picture. Specifically, we study the correlation of picture owner gender with alt-text, and Emojis/Emoticons used by commenters when reacting to these pictures. We leverage this image sharing and reaction mode of Facebook users to derive an efficient and accurate technique for user gender inference. We show that such a privacy attack often succeeds even when other information than pictures published by their owners is either hidden or unavailable

Inferring attributes with picture metadata embeddings

International audienceUsers in online social networks are vulnerable to attribute inference attacks due to some published data. Thus, the picture owner's gender has a strong influence on individuals' emotional reactions to the photo. In this work, we present a graph-embedding approach for gender inference attacks based on pictures meta-data such as (i) alt-texts generated by Facebook to describe the content of images, and (ii) Emojis/Emoticons posted by friends, friends of friends or regular users as a reaction to the picture. Specifically, we apply a semi-supervised technique, node2vec, for learning a mapping of pictures meta-data to a low-dimensional vector space. Next, we study in this vector space the gender closeness of users who published similar photos and/or received similar reactions. We leverage this image sharing and reaction mode of Facebook users to derive an efficient and accurate technique for user gender inference. Experimental results show that privacy attack often succeeds even when other information than pictures published by their owners is either hidden or unavailable

What your Facebook Profile Picture Reveals about your Personality

People spend considerable effort managing the impressions they give others. Social psychologists have shown that people manage these impressions differently depending upon their personality. Facebook and other social media provide a new forum for this fundamental process; hence, understanding people's behaviour on social media could provide interesting insights on their personality. In this paper we investigate automatic personality recognition from Facebook profile pictures. We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals. For example, extroverts and agreeable individuals tend to have warm colored pictures and to exhibit many faces in their portraits, mirroring their inclination to socialize; while neurotic ones have a prevalence of pictures of indoor places. Then, we propose a classification approach to automatically recognize personality traits from these visual features. Finally, we compare the performance of our classification approach to the one obtained by human raters and we show that computer-based classifications are significantly more accurate than averaged human-based classifications for Extraversion and Neuroticism

An Empirical Study on Android for Saving Non-shared Data on Public Storage

With millions of apps that can be downloaded from official or third-party market, Android has become one of the most popular mobile platforms today. These apps help people in all kinds of ways and thus have access to lots of user's data that in general fall into three categories: sensitive data, data to be shared with other apps, and non-sensitive data not to be shared with others. For the first and second type of data, Android has provided very good storage models: an app's private sensitive data are saved to its private folder that can only be access by the app itself, and the data to be shared are saved to public storage (either the external SD card or the emulated SD card area on internal FLASH memory). But for the last type, i.e., an app's non-sensitive and non-shared data, there is a big problem in Android's current storage model which essentially encourages an app to save its non-sensitive data to shared public storage that can be accessed by other apps. At first glance, it seems no problem to do so, as those data are non-sensitive after all, but it implicitly assumes that app developers could correctly identify all sensitive data and prevent all possible information leakage from private-but-non-sensitive data. In this paper, we will demonstrate that this is an invalid assumption with a thorough survey on information leaks of those apps that had followed Android's recommended storage model for non-sensitive data. Our studies showed that highly sensitive information from billions of users can be easily hacked by exploiting the mentioned problematic storage model. Although our empirical studies are based on a limited set of apps, the identified problems are never isolated or accidental bugs of those apps being investigated. On the contrary, the problem is rooted from the vulnerable storage model recommended by Android. To mitigate the threat, we also propose a defense framework

Personality perception based on LinkedIn profiles

__Purpose:__ Job-related social networking websites (e.g. LinkedIn) are often used in the recruitment process because the profiles contain valuable information such as education level and work experience. The purpose of this paper is to investigate whether people can accurately infer a profile owner’s self-rated personality traits based on the profile on a job-related social networking site. __Design/methodology/approach:__ In two studies, raters inferred personality traits (the Big Five and self-presentation) from LinkedIn profiles (total n=275). The authors related those inferences to self-rated personality by the profile owner to test if the inferences were accurate. __Findings:__ Using information gained from a LinkedIn profile allowed for better inferences of extraversion and self-presentation of the profile owner (r’s of 0.24-0.29). __Practical implications:__ When using a LinkedIn profile to estimate trait extraversion or self-presentation, one becomes 1.5 times as likely to actually select the person with higher trait extraversion compared to the person with lower trait extraversion. __Originality/value:__ Although prior research tested whether profiles of social networking sites (such as Facebook) can be used to accurately infer self-rated personality, this was not yet tested for job-related social networking sites (such as LinkedIn). The results indicate that profiles at job-related social networks, in spite of containing only relatively standardized information, “leak” information about the owner’s personality

Inferring Social Media Users’ Demographics from Profile Pictures: A Face++ Analysis on Twitter Users

In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ human evaluations to manually tag both the set of images that were determined to contain faces and the set that was determined not to contain faces, comparing the results to Face++. Of the thirty percent that Face++ identified as containing a face, about 80% are more likely than not the account holder based on our manual classification, with a variety of issues in the remaining 20%. Of the images that Face++ was unable to detect a face, we isolate a variety of likely issues preventing this detection, when a face actually appeared in the image. Overall, we find the applicability of automatic facial recognition to infer demographics for system development to be problematic, despite the reported high accuracy achieved for image test collection

Teens, Social Media, and Privacy

Teens share a wide range of information about themselves on social media sites; indeed the sites themselves are designed to encourage the sharing of information and the expansion of networks. However, few teens embrace a fully public approach to social media. Instead, they take an array of steps to restrict and prune their profiles, and their patterns of reputation management on social media vary greatly according to their gender and network size

Teens, Kindness and Cruelty on Social Network Sites

Analyzes survey findings about how teenagers navigate the world of "digital citizenship," including experiences of, reactions to, and sources of advice about online cruelty; privacy controls and practices; and levels of parental regulation