Circles, posts and privacy in egocentric social networks

Gao, Bo; Berendt, Bettina

doi:10.1145/2492517.2492654

Cited by 15 publications

(14 citation statements)

References 18 publications

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“…The user-centric evaluations we performed [5,12] suggest that the tools are a good support for their respective tasks and well-liked by users. Still, we believe that the tasks they "really" support (building up knowledge about scientific areas, communicating over a social-networking site) are more complex and longerterm than those that can be tested in the usual form of one-shot laboratory user studies, and that this should be investigated in future work [25].…”

Section: Evaluating Interactive Uses Of Grouping Guidesmentioning

confidence: 92%

“…An example of this is shown in [5]. Second, the interactive tool should ideally produce something that is indeed useful for the user's current task -to measure this goal, one must also measure other criteria like "are the groups semantically meaningful in the application domain" [5] or "do the groups support another relevant behaviour in the application domain" [12].…”

Section: Evaluating Interactive Uses Of Grouping Guidesmentioning

confidence: 99%

“…5 (right). It uses modularitybased clustering of the user's Facebook friend graph for the initial grouping, a choice motivated by a requirements analysis and validated by a comparison with other state-of-the-art algorithms on a ground-truth data set [12].…”

mentioning

confidence: 99%

See 2 more Smart Citations

How to Carve up the World: Learning and Collaboration for Structure Recommendation

Verbeke

Subasic

Berendt

2013

Ubiquitous Social Media Analysis

Self Cite

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Abstract. Structuring is one of the fundamental activities needed to understand data. Human structuring activity lies behind many of the datasets found on the internet that contain grouped instances, such as file or email folders, tags and bookmarks, ontologies and linked data. Understanding the dynamics of large-scale structuring activities is a key prerequisite for theories of individual behaviour in collaborative settings as well as for applications such as recommender systems. One central question is to what extent the "structurer" -be it human or machine -is driven by his/its own prior structures, and to what extent by the structures created by others such as one's communities. In this paper, we propose a method for identifying these dynamics. The method relies on dynamic conceptual clustering, and it simulates an intellectual structuring process operating over an extended period of time. The development of a grouping of dynamically changing items follows a dynamically changing and collectively determined "guiding grouping". The analysis of a real-life dataset of a platform for literature management suggests that even in such a typical "Web 2.0" environment, users are guided somewhat more by their own previous behaviour than by their peers. Furthermore, we also illustrate how the presented method can be used to recommend structure to the user.

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Section: Evaluating Interactive Uses Of Grouping Guidesmentioning

confidence: 92%

Section: Evaluating Interactive Uses Of Grouping Guidesmentioning

confidence: 99%

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How to Carve up the World: Learning and Collaboration for Structure Recommendation

Verbeke

Subasic

Berendt

2013

Ubiquitous Social Media Analysis

Self Cite

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“…1 and 2), users also employ other strategies (Jones and O'Neill, 2010;Gao and Berendt, 2013). These strategies were also translated into different audience visualizations in FreeBu #2.…”

Section: Other Audience Visualizationsmentioning

confidence: 99%

The promise of audience transparency. Exploring users’ perceptions and behaviors towards visualizations of networked audiences on Facebook

Wolf

Gao

Berendt

et al. 2015

Telematics and Informatics

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a b s t r a c tThe presence of multiple audiences and the collapse of boundaries between them in Facebook make it difficult for users to know and to control who has access to their online contributions. Previous research has shown how visualizations of Facebook friends are useful, but mainly focused on the instrumental goal of controlling access. It is unclear, however, what value users themselves see in visualizations and whether knowledge and/or control are important to them. In this research, these questions were studied by evaluating FreeBu, a semi-automatic and interactive grouping technology that visualizes Facebook friends. The results indicate that audience visualizations are especially perceived useful for grouping and reflection purposes. Moreover, we found how users are attracted to larger groups, those with whom they communicate more, bridges and outliers in their network. The combined findings suggest that awareness is considered at least as important as control. Therefore, a shift from audience control to audience transparency is recommended.

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“…Because friends on SNSs are not always very close, but may include hundreds of acquaintances and even strangers, users may face difficulty in determining the reach of their information, and as a result can experience accidental over disclosure, regrets, embarrassments, and other social problems. [9][10][11] However, many have learned cautious behavior and keep everything truly personal or private off on SNSs, users desire to adapt the mechanism sharing information differently with different group of people, as we do in real world.…”

Section: Introductionmentioning

confidence: 99%

Predicting the dynamics of social circles in ego networks using pattern analysis and GA K‐means clustering

Agarwal

Bharadwaj

2015

WIREs Data Min & Knowl

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The tremendous amount of content generated on online social networks has led to a radical paradigm shift in the interest of people to group friends dynamically and share content selectively. At large, social networking sites (e.g. Google+, Facebook, Twitter, etc.) offer users with various controls over categorizing their family members, friends, colleagues, etc. into one or more ‘circles’ that they want to share content with. However, it is typically impossible to design social circles in large networks and update their number and size, whenever networks grow. Aiming at predicting the dynamics of formation and evolution of social circles, we performed several experiments on ground‐truth data, and found that studying patterns of network and profile features at individual level rather than studying circle as a whole can greatly enhance the understanding of social circles development in online social networks. In this review, we first present a comprehensive study of the structural behavior of circles, and then build an observation that within every circle there exist some key elements, termed as ‘Node of Creations (NoCs)’, which play an important role in the development, survival, and evolvability of circle structures. We, therefore, propose a Genetic Algorithm–based framework to determine these key elements (NoCs) and differentiate Ego networks into non‐overlapping, hierarchically nested as well as overlapping circles by leveraging knowledge from the identified patterns in order to assist K‐means clustering. We have performed our experiments using Facebook and Twitter datasets and the experimental results clearly demonstrate the effectiveness of our scheme. WIREs Data Mining Knowl Discov 2015, 5:113–141. doi: 10.1002/widm.1150 This article is categorized under: Technologies > Machine Learning Technologies > Prediction Technologies > Structure Discovery and Clustering

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Circles, posts and privacy in egocentric social networks

Cited by 15 publications

References 18 publications

How to Carve up the World: Learning and Collaboration for Structure Recommendation

How to Carve up the World: Learning and Collaboration for Structure Recommendation

The promise of audience transparency. Exploring users’ perceptions and behaviors towards visualizations of networked audiences on Facebook

Predicting the dynamics of social circles in ego networks using pattern analysis and GA K‐means clustering

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