skip to main content


The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 11:00 PM ET on Thursday, May 23 until 2:00 AM ET on Friday, May 24 due to maintenance. We apologize for the inconvenience.

This content will become publicly available on October 19, 2024

Title: Investigating User Photo Privacy Settings on Instagram

Photo sharing has become increasingly easy with the rise of social media. Social networking sites (SNSs), such as Instagram and Facebook, are well known for their image-sharing capabilities. However, this brings the concern of photo privacy, such as who may see the images of a user who is included in a post. Photo privacy settings offer detailed and more secure ways to share a user’s photos, however, this would require SNS users to understand these settings. To better grasp users’ understanding of photo privacy settings, we conducted a structured interview with Instagram users. We found that users were aware of the majority of the privacy settings asked about and that they accurately perceived their photo privacy safety based on their knowledge of photo privacy settings.

more » « less
Author(s) / Creator(s):
 ;  ;  ;  
Publisher / Repository:
SAGE Publications
Date Published:
Journal Name:
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Medium: X Size: p. 2291-2292
["p. 2291-2292"]
Sponsoring Org:
National Science Foundation
More Like this
  1. Hashtags can greatly facilitate content navigation and improve user engagement in social media. Meaningful as it might be, recommending hashtags for photo sharing services such as Instagram and Pinterest remains a daunting task due to the following two reasons. On the endogenous side, posts in photo sharing services often contain both images and text, which are likely to be correlated with each other. Therefore, it is crucial to coherently model both image and text as well as the interaction between them. On the exogenous side, hashtags are generated by users and different users might come up with different tags for similar posts, due to their different preference and/or community effect. Therefore, it is highly desirable to characterize the users’ tagging habits. In this paper, we propose an integral and effective hashtag recommendation approach for photo sharing services. In particular, the proposed approach considers both the endogenous and exogenous effects by a content modeling module and a habit modeling module, respectively. For the content modeling module, we adopt the parallel co-attention mechanism to coherently model both image and text as well as the interaction between them; for the habit modeling module, we introduce an external memory unit to characterize the historical tagging habit of each user. The overall hashtag recommendations are generated on the basis of both the post features from the content modeling module and the habit influences from the habit modeling module. We evaluate the proposed approach on real Instagram data. The experimental results demonstrate that the proposed approach significantly outperforms the state-of-theart methods in terms of recommendation accuracy, and that both content modeling and habit modeling contribute significantly to the overall recommendation accuracy. 
    more » « less
  2. With the rising popularity of photo sharing in online social media, interpersonal privacy violations, where one person violates the privacy of another, have become an increasing concern. Although applying image obfuscations can be a useful tool for improving privacy when sharing photos, prior studies have found these obfuscation techniques adversely affect viewers' satisfaction. On the other hand, ephemeral photos, popularized by apps such as Snapchat, allow viewers to see the entire photo, which then disappears shortly thereafter to protect privacy. However, people often use workarounds to save these photos before deletion. In this work, we study people's sharing preferences with two proposed 'temporal redactions', which combines ephemerality with redactions to allow viewers to see the entire image, yet make these images safe for longer storage through a gradual or delayed application of redaction on the sensitive portions of the photo. We conducted an online experiment (N=385) to study people's sharing behaviors in different contexts and under different levels of assurance provided by the viewer's platform (e.g., guaranteeing temporal redactions are applied through the use of 'trusted hardware'). Our findings suggest that the proposed temporal redaction mechanisms are often preferred over existing methods. On the other hand, more efforts are needed to convey the benefits of trusted hardware to users, as no significant differences were observed in attitudes towards 'trusted hardware' on viewers' devices. 
    more » « less
  3. In recent years, Online Social Networks (OSN) have become popular content-sharing environments. With the emergence of smartphones with high-quality cameras, people like to share photos of their life moments on OSNs. The photos, however, often contain private information that people do not intend to share with others (e.g., their sensitive relationship). Solely relying on OSN users to manually process photos to protect their relationship can be tedious and error-prone. Therefore, we designed a system to automatically discover sensitive relations in a photo to be shared online and preserve the relations by face blocking techniques. We first used the Decision Tree model to learn sensitive relations from the photos labeled private or public by OSN users. Then we defined a face blocking problem and developed a linear programming model to optimize the tradeoff between preserving relationship privacy and maintaining the photo utility. In this paper, we generated synthetic data and used it to evaluate our system performance in terms of privacy protection and photo utility loss. 
    more » « less
  4. Social media services and location-based photo-sharing applications, such as Flickr, Twitter, and Instagram, provide a promising opportunity for studying tourist behaviors and activities. Researchers can use public accessible geo-tagged photos to map and analyze hotspots and tourist activities in various tourist attractions. This research studies geo-tagged Flickr photos collected from the Grand Canyon area within 12 months (2014/12/01–2015/11/30) using kernel density estimate (KDE) mapping, Exif (Exchangeable image file format) data, and dynamic time warping (DTW) methods. Different spatiotemporal movement patterns of tourists and popular points of interests (POIs) in the Grand Canyon area are identified and visualized in GIS maps. The frequency of Flickr’s monthly photos is similar (but not identical) to the actual tourist total numbers in the Grand Canyon. We found that winter tourists in the Grand Canyon explore fewer POIs comparing to summer tourists based on their Flickr data. Tourists using high-end cameras are more active and explore more POIs than tourists using smart phones photos. Weekend tourists are more likely to stay around the lodge area comparing to weekday tourists who have visited more remote areas in the park, such as the north of Pima Point. These tourist activities and spatiotemporal patterns can be used for the improvement of national park facility management, regional tourism, and local transportation plans. 
    more » « less
  5. ‘Interdependent’ privacy violations occur when users share private photos and information about other people in social media without permission. This research investigated user characteristics associated with interdependent privacy perceptions, by asking social media users to rate photo-based memes depicting strangers on the degree to which they were too private to share. Users also completed questionnaires measuring social media usage and personality. Separate groups rated the memes on shareability, valence, and entertainment value. Users were less likely to share memes that were rated as private, except when the meme was entertaining or when users exhibited dark triad characteristics. Users with dark triad characteristics demonstrated a heightened awareness of interdependent privacy and increased sharing of others’ photos. A model is introduced that highlights user types and characteristics that correspond to different privacy preferences: privacy preservers, ignorers, and violators. We discuss how interventions to support interdependent privacy must effectively influence diverse users. 
    more » « less