Hello, everyone. We are a team of researchers from Penn State Media Effects Lab and University of Southern California. My name is Yuan, and I would like to share with you our study of how health recommender systems based on social media can affect user trust.
The use of scalable digital health interventions has the potential to significantly enhance the efficiency and accessibility of health care.
However, the vast amount of user data scattered across different sites presents significant challenges to delivering highly relevant information to users, thus giving rise to health recommender systems.
Currently, there are three commonly used personalization approaches. The first approach is demographic filtering. For example, some Health RS provide motivational information that is tailored based on users' age and gender,
Some Health RS apply content-based filtering, such that matches items based on users’ own workout preferences;
Other recommender systems use collaborative filtering and provides recommendations based on data from similar users.
However, these RS often struggle when there is limited user data - known as the "cold start" problem - and infer user preferences based on implicit user relationships rather than actual activity.
In these cases, social media networks can provide additional information about connections among users thereby leading to more reliable and accurate recommendations.
Different from the traditional types of approaches that rely on obtaining user data from their activities on a website, in recent years, online recommender systems have leveraged the power of social influence for promoting healthy behaviors.
The prevalence of social login has also accelerated the process of user profile building by granting web application providers access to users' social network credentials.
Is this a better way to provide health recommendations? Existing studies have shown that recommendations may trigger negative outcomes. If users perceived that recommendation algorithms filtered out or suppress certain social identities.
and users are very sensitive to their social media data. All this could potentially trigger resistance to behavior change.
In fact, users may have their own preferences regarding how their personalized recommendations should be generated.
While in reality, users often have no such freedom to make a choice. but rather accept the system-assigned algorithms.
Currently, there still remains a lack of understanding of how different personalization approaches differ in their persuasiveness for health recommendations, and how they may elicit negative reactions from users.
In addition, what are some design mechanisms to overcome such negative reactions are limited.
to examine these questions, the study follows the theoretical framework of HAII-TIME. It proposed to theoretical routes.
The "cue route "examines how specific interface queues of an AI system can trigger quick judgments about the credibility of the system.
for example, and recommend a system interface indicating that the recommendation is based on a popular choice of other users, may trigger the "Bandwagon heuristic".
or "if others think something is good, I should think so, too", which leads to a positive user perception.
The "action route", on the other hand, affect user judgment by affording users the ability to engage with the system.
For example, users can change the system settings or make different choices to affect how the system works.
In our study, we designed a prototype website named "My Fitness" for recommending personalized fitness plans.
And there are 2 types of user data. One is in-app data, another one is social media data.
For in-app data, the recommender system used the answers to the questions users have provided, such as users' fitness preferences, and demographic information.
For social media data, users log in through their social media accounts and the RS told the users that they will receive recommendations based on their social media information.
We designed a 6 by 2 between-subjects online experiment.
We provided 6 different personalization approaches which differs from the source of data, which is in app or social media and different approaches of personalization.
We also design a user a choice button that users either have the options to select from additional personalization approaches.
Or they don't have such choices. So in our study half of the participants were randomly assigned to the personalization conditions with this user choice condition, while the other half do not have such option.
By the end of the interaction, participants received their personalized fitness plan, which we designed from the Department of Health and Human Services.
To avoid the confounding effects, they received the same recommendations across different conditions.
To manipulate different personalization types. What we did is we embedded different explanations that the respective system presented to users.
For example, if it is the in-app content based filtering, the system will say "the personalized workout plan is based on users own preferences for the type, intensity, and frequency of exercise."
For social media-collaborative filtering, the system will say that the workout plan is based on "what the users social media friends have viewed and liked on social media."
And such manipulations appeared 3 times on the homepage, loading page, and result page.
We first examined the log data from user interactions with the user choice feature to understand how users switched among different algorithms.
And we found that 67.5% of the participants switched
to another personalization approach. Well, 32.5% chose to stick with the current approach.
Meanwhile, while users had the choice, most of the users chose to switch to generate their fitness plan based on their own preferences.
Users chose the social media-based recommendations the least
Our data further shows that collaborative filtering using social media data led to the highest level of perceived threat to identity.
Content-based filtering using user fitness preferences led to the lowest level of threat to identity
Through mediation analysis, we also found that personalization approaches using social media data will lead to greater perceived privacy risk and thereby lower user trust.
collaborative filtering based on social media data such as "based on what your social media friends have viewed and liked" scored the highest for perceived privacy risk.
In addition, we found that providing user choice can enhance users’ sense of agency and mitigate threat to identity, which enhanced user trust.
such effects were more profound for users who were assigned to the social media-collaborative filtering condition
We also found that the main effect of user choice on usability was not significant
indicating that user choice does not negatively affect usability
We also gain some insights through open-ended questions regarding how users think of social media based recommendations.
Multiple participants indicated that data derived from their social media activities would not accurately reflect their unique health needs.
For example, one participant mentioned that "we all hit 'Like' on things we don't care about, and that could skew the effectiveness of the algorithm."
Several participants also mentioned another limitation of such approach if they don't have enough fitness information from their searches, or viewing of social media to generate a proper fitness plan personalized to them.
In relation to percived privacy risk, several participants voiced concerns regarding social media data use,
and they also mentioned that it will negatively impact their friends.
Taking together, our study demonstrate that providing recommendations based on social media may backfire in the health context by posing a threat to users sense of
Therefore, current practices that are embedded in social media feeds, such as “your friends . . . recently liked this post,” may not be an effective strategy for encouraging healthy outcomes.
Our study calls for a careful evaluation of user perceptions when implementing social network information into personalization algorithms.
Potential design implications include incorporating interface cues such as “based on your unique preferences” to boost users’ sense of identity and improve their user experience of the RS.
The study also reveals the positive effects of providing user choice in enhancing users’ sense of agency, which in turn enhanced their trust in the system.
For details, please refer to our paper. Thank you so much for liste ning! Please let us know if you have any questions or comments.