# OmniWedges: Improved Radar-Based Audience Selection for Social Networks

Frederic Raber, Antonio Krüger

► To cite this version:

Frederic Raber, Antonio Krüger. OmniWedges: Improved Radar-Based Audience Selection for Social Networks. 17th IFIP Conference on Human-Computer Interaction (INTERACT), Sep 2019, Paphos, Cyprus. pp.654-658, 10.1007/978-3-030-29390-1_56. hal-02878626

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Submitted on 23 Jun 2020

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# OmniWedges: Improved Radar-Based Audience Selection for Social Networks

Frederic Raber¹ and Antonio Krüger¹

DFKI Saarland Informatics Campus, Saarbrücken, Germany
{frederic.raber},{krueger}@dfki.de

**Abstract.** Selecting the right audience for Facebook posts is a task that users often skip, resulting in unwanted post disclosure or avoidance of sharing sensitive posts. We present OmniWedges, a user interface designed to allow users of online social networks to make meaningful decisions on who to share their posts with. Our study results also show that with all Facebook friends, the error rate can be significantly reduced compared to the Facebook interface. In an interview, we were also able to spot a change in posting behavior and frequency with our interface.

## 1 Introduction

The current designs of the social network sites require users to scroll through their endless friend lists, or even to remember which of them they want to include or exclude from their post. Therefore most posts are shared with all of the user's friends, even though this is often not the best idea [3]. Radar interfaces have been shown to provide a better overview on the privacy settings [2] which furthermore engages users in adjusting their privacy settings more regularly [2]. Nevertheless, radar interfaces often suffer a space problem, in contrast to a list, which can be extended endlessly [6]. Especially when it comes to selecting the audience for social network posts, the number of potential recipients, which is the friend list of the user, can contain up to thousands of users. In our work, we took this use-case as an example to find out *How radar interfaces can be enhanced to be able to display a large amount of data items and Whether a radar interface is suitable to define the post audience for social networks*. Our interface "OmniWedges" uses a radar metaphor for aligning the users' friends around the user, based on their interpersonal distance and friend groups they belong to. OmniWedges is highly scalable and offers several functionalities to enable displaying *all* of a user's hundreds or thousands of friends while still providing an overview of who is selected and who is not. In a comparative user study with the Facebook custom privacy setting interface as a control condition, we found that OmniWedges is able to reduce the amount of errors made with *all* of the users' Facebook friends.

## 2 Related Work

Despite the existence of a privacy UI, social network users often do not use them at all [3]. The UIs are perceived as cumbersome and do not scale with respect to usual numbers of friends in a social network [3]. Social network providers therefore introduced lists or
"circles" of friends, enabling users to create lists containing subsets of their online friends, and share posts only with those. Creating these lists manually comes at a significantly increased users burden [5]. Radar interfaces have been proven to be highly effective for such tasks, like getting an overview on data shared inside an intelligent retail store [7]. In our work, we try to adapt this metaphor on the domain of social network audience selection, which introduces one major obstacle, namely the high amount of data items (firends) to display in the limited space of the UI.

## 3 OmniWedges

OmniWedges offers a graphical user interface, which allows audience selection based on interpersonal distance, for different groups. The interface contains the profile picture for each social network friend, later denoted as "friend picture". Each of the user's friend groups is represented by a wedge in the UI. The friend pictures are aligned around the center according to the *tie strength* between the friend and the user. The current implementation of OmniWedges uses the tie strength calculation and friend groups offered by the Facebook website. To reduce side effects, we let the participants review and adapt the friend lists and tie strength ordering in the experiment. Initially, no friends are selected. The user clicks and drags from the center of a wedge to the outer rim to select a subset of friends as recipients for a post. The selected area is colored grayish. All friends which are inside the selected area of the wedge (from here on called the "wedge area") will receive the post. The number of selected friends and a list of the ten closest friends that are selected is displayed below the graph. In the bottom left corner, there are two buttons for selecting all friends and for resetting the selection. All selection possibilities of OmniWedges are shown in Figure 1.

Radar interfaces often suffer from the limited space available in the radar UI, which limits the number of displayable friends in our case. **We therefore implemented several functionalities in order to reduce the problem of limited space within our UI:** Only a small subset of the Facebook friends are real friends that are of importance for recipient selection [1]. We therefore decrease the size of the friend images with decreasing tie strength, so that the closest friends gain the most importance ("Incremental picture size"). Using a double click, it is possible to zoom into and out of a certain area of the wedge to have a better overview, especially in crowded areas ("Zooming"). Based on the number of friends inside a wedge, OmniWedges selects some of the friend images (every second, third, fourth...) as lighthouse images that can be used as orientation points for the selection. All other friend images are shrunk to a small dot to avoid crowding ("lighthouse design").

## 4 User study

The study was performed with 20 participants using a within-subject design: Half of the subjects started with the Facebook interface and continued with OmniWedges, and vice versa for the other half of the subjects. At the day of the study, users had to organize the friends from their own Facebook profile into friend groups, and to sort them with ascending tie strength. **We gave no constraints on the number of friend groups, and**
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Actions for the different use cases in OmniWedges: Select all friends up to a given tie strength (A), select friends of one or more friend groups up to a given tie strength (B), exclude friends up to a given tie strength (C), select multiple areas inside a friend group (D) and Select/deselect single friends (E)</figcaption></figure>

used their Facebook friends as groups for the whole rest of the experiment. The user was then given 12 tasks to solve for each interface: Six explicit tasks (for example “select the 20 closest friends”) and six implicit tasks, where the subjects were given sensitive posts like “Please imagine you want to share pictures of a party that caused you to miss your family’s Thanksgiving event”. After each condition, the user was given a list of all friends with an indication whether the friend was selected or not. The user then had to identify friends that were included by mistake (false positive) or mistakenly not included as a recipient (false negative). The users had to fill in an AttrakDiff [4] usability questionnaire after each condition, and were given a five-minute break to rest and recover. The experiment closed with a semi-structured interview, where we tried to find out whether they would use OmniWedges on Facebook, and whether their posting behavior might change.

The number of Facebook friends of the participants ranged from 53 to 1260 (mean 437). As the amount is highly variable, we normalized the amount of false positives and negatives by dividing them by the number of friendlist entries. We performed a 2(condition) x 2(explicit or implicit task) x 2(false positive or false negative) ANOVA to compare the errors made throughout the study. Taking only the interface as an effect, the results indicate that significantly fewer errors are made using OmniWedges ($F = 5.57, p = 0.031, M_{Wedges} = 0.0076, M_{FB} = 0.020$). The type of task, explicit or implicit, did not have any significant effect on the error rate($F = 0.677, p = 0.423$). OmniWedges outperforms its Facebook counterpart in terms of attractiveness ($T = 6.115, p < 0.001, M_{Wedges} = 5.37, M_{FB} = 3.35$) and hedonic quality ($T_{HQ-I} = 4.93, p_{HQ-I} < 0.001, M_{HQ-I-Wedges} = 5.09, M_{HQ-I-FB} = 3.66; T_{HQ-S} = 7.83, p_{HQ-S} < 0.001, M_{HQ-S-Wedges} = 5.26, M_{HQ-S-FB} = 3.17$) with high significance, assuring a better user experience. There is also a tendency for a higher pragmatic quality using OmniWedges ($T = 1.83, p = 0.082, M_{Wedges} = 4.75, M_{FB} = 4.06$). 50% of the users stated they preferred OmniWedges with an additional search field for finding specific friends, 33% preferred a combination of OmniWedges and the Facebook interface, and 8.3% wished to keep only OmniWedges or the Facebook interface, respectively. 85.7% stated they would change their posting behavior when using OmniWedges.
those who would, 88.9% would do more narrowcasting, and the remaining 11.1% would post more sensitive posts.

# 5 Discussion and outlook

In contrast to earlier work [6], we introduced several UI mechanisms that allow the display of all of a user's friends inside our radar interface. The results of the study show that, using these improvements, the radar metaphor can also be used to display a large number of friends while still reducing the amount of errors. According to the interview results, a majority of users would replace the current Facebook UI with a version of OmniWedges. The interview answers also indicate a change in users' mental model: Most users tend to do more narrowcasting, or publish more sensitive posts. This may be caused by a different awareness of the post audience: Rather than always displaying only a small portion of all friends at once in a scrollable list, the radar interface displays *all* of a user's friends at once, allowing them to have an overview of the large number of friends that would receive the post, possibly resulting in a higher trust in the UI. Therefore, users begin to think about whether this large audience is really the desired audience for their post, resulting in a more rigorous limitation of the post audience with OmniWedges. As a first step in future work, we would like to integrate our approach into a social network website and evaluate the usage frequency of our tool against the standard audience selection functionality, especially when using the already existing friend groups (created either automatically or by the user) and the tie strength calculation offered by the social network provider.

This work was supported by the German Research Foundation (DFG) via the collaborative research center "Methods and Tools for Understanding and Controlling Privacy" (SFB 1223), project A7.

# References

1. Christakis, N.A., Fowler, J.H.: Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives. Back Bay Books (2011)

2. Christin, D., Reinhardt, A., Hollick, M., Trumpold, K.: Exploring user preferences for privacy interfaces in mobile sensing applications. MUM '12, ACM, New York, NY, USA (2012)

3. Gross, R., Acquisti, A.: Information revelation and privacy in online social networks. pp. 71–80. WPES '05, ACM, New York, NY, USA (2005)

4. Hassenzahl, M., Burmester, M., Koller, F.: Attrakdiff: Ein fragebogen zur messung wahrgenommener hedonischer und pragmatischer qualitaet. In: MuC 2003. B. G. Teubner

5. Paul, T., Puscher, D., Strufe, T.: Improving the usability of privacy settings in facebook. CoRR (2011)

6. Raber, F., Luca, A.D., Graus, M.: Privacy wedges: Area-based audience selection for social network posts. In: SOUPS. Denver, CO (2016)

7. Raber, F., Vossebein, N.: Uretail: Privacy user interfaces for intelligent retail stores. In: Interact '17. pp. 473–477 (2017)