=Paper=
{{Paper
|id=Vol-2482/paper50
|storemode=property
|title=The Challenge of Personal Attribute Preferences in Recommending
Diverse, Reliable News Sources
|pdfUrl=https://ceur-ws.org/Vol-2482/paper50.pdf
|volume=Vol-2482
|authors=Brooke Auxier,Jennifer Golbeck
|dblpUrl=https://dblp.org/rec/conf/cikm/AuxierG18
}}
==The Challenge of Personal Attribute Preferences in Recommending
Diverse, Reliable News Sources==
https://ceur-ws.org/Vol-2482/paper50.pdf
The challenge of personal attribute preferences in recommending
diverse, reliable news sources
Brooke Auxier Jennifer Golbeck
Philip Merrill College of Journalism School of Information Studies
University of Maryland University of Maryland
College Park, Maryland, USA College Park, Maryland, USA
bauxier@umd.edu jgolbeck@umd.edu
ABSTRACT
The affordances of social media and the internet allow users to Online news audiences have a vast amount of content constantly
encounter and engage with diverse and novel news content. available at their fingertips. In order to manage this amount of
However, user preference and bias may limit the content consumed
in these spaces. Using signals of reliability, which have been information and avoid information overload, audiences make
studied as they relate to content and information sharers in social decisions about which content and sources to trust, read, follow and
media environments, a recommender system could be built to share. However, research suggests that audiences may do this in a
suggest news content to users. However, as stated, assessments of way that limits their access to a diverse range of sources and
trustworthiness and reliability come with some user bias and an viewpoints [3], [4], as audiences are likely to actively engage with
algorithm that uses these preferences could be extremely limiting. content that confirms their existing beliefs [5], [6], [7].
We propose the following solutions: (1) using trusted social
connections to surface content; (2) using bots to broaden a user’s This is exacerbated when personalization algorithms are at work.
information ecosystem. Algorithms are designed to recommend content that users will like
and engage with. If users are personally driven toward a small set
of perspectives, “accurate” algorithms may drive them deeper into
that space. Beyond just the issue of viewpoint, user preferences -
and biases - with respect to the people sharing content also matters.
On many social media platforms, algorithms highlight news and
other content posted by user accounts, not just organizational
accounts. Thus, we must consider how users will respond to the
KEYWORDS people sharing the content as well.
news recommendations, Reliability, News sources, Social media
ACM Reference format: 2 Related work
Research suggests then when social media users encounter
information sharers on the platform who are unfamiliar to them,
they use multiple heuristic cues to gauge the trustworthiness and
credibility of the source, as well as the message. Earlier work by
[8] found that tweet content with non-standard grammar and
1 Introduction punctuation were viewed as having low credibility, whereas tweets
that included links to high quality sources were seen as more
Billions of people across the globe use social media sites. In 2016, credible.
2.28 billion people were on social media and that number is
expected to increase to 3.02 billion by 2021 [1]. Though there are Audiences also judge the information sharers themselves. Users
many reasons for using social media, news consumption is common that had a default image, cartoon or avatar as their profile photos
activity on the sites. In the U.S, 67 percent of adults report getting were rated as having low credibility, whereas users with high
at least some of their news on social media sites [2]. Though these follower counts who had a Twitter bio were seen as more credible
figures are already substantial, the numbers undoubtedly increase [8]. The same study found that the sharer’s @handle or screen name
when news usage metrics from mobile apps, messaging platforms also influenced perceived credibility. Topically relevant screen
like WhatsApp, WeChat and GroupMe, and news websites are also names were seen as more credible than ‘internet style names.’
considered.
Similar, more recent research we conducted suggests that users find
certain features of social media profiles more reliable than others
[9]. For example, the study (N=261) found that when exposed to
Copyright © CIKM 2018 for the individual papers by the papers'
authors. Copyright © CIKM 2018 for the volume as a collection
by its editors. This volume and its papers are published under
the Creative Commons License Attribution 4.0 International (CC
BY 4.0).
neutral news-oriented Twitter-like content from unknown by incorporating profile attribute preferences. This would need
information sharers, users with Western names, gender-neutral experimental validation. However, any improved performance may
names and female avatars were perceived as most reliable. come at the cost of bias in the type of sources and in the diversity
Respondents were also most likely to share content from of news a person sees. Countering user bias through
information sharers with Western names, gender-neutral names and recommendation and leveraging the perceived reliability of a
who had non-human avatars (e.g. logos, non-human objects). profile to bring more diverse news to users are both interesting
research challenges going forward. They highlight the complexity
of social interaction and the role it plays in recommendation, but
also the opportunities that arise from a deeper understanding of how
3 Proposed solutions, recommendations users assess people they encounter online.
These signals of reliability could be integrated into a recommender
system to suggest content to users. If we know they are likely to
engage more with posts from people with certain profile features, REFERENCES
an algorithm could be designed to bring more content from those [1] eMarketer, Number of social media users worldwide from 2010 to 2021 (in
billions), https://www.statista.com/statistics/278414/number-of-worldwide-
types of accounts. social-network-users/
[2] Elisa Shearer and Jeffrey Gottfried. News Use Across Social Media Platforms,
However, one can see that there is some bias in these evaluations http://www.journalism.org/2017/09/07/news-use-across-social-media-
platforms-2017/
of trustworthiness and credibility. There are plenty of reliable, [3] Seth Flaxman, Sharad Goel and Justin M. Rao. (2016). Filter Bubbles, Echo
trustworthy and talented journalists and who do not have Western Chambers, and Online News Consumption. Public Opinion Quarterly, 80(S1),
sounding names and who use cartoon avatars in their profile photos 298-320.
- though subjects in our study gave these types of users among the [5] Thomas J. Johnson, Shannon Bichard and Weiwu Zhang. (2009).
Communication Communities or “CyberGhettos?”: A Path Analysis Model
lowest credibility ratings. There are also many nefarious sources Examining Factors that Explain Selective Exposure to Blogs. Journal of
with profiles that match the traditional standards of trustworthiness. Computer-Mediated Communication, 15(1), 60-82.
[6] Solomon Messing and Sean J. Westwood. (2014). Selective Exposure in the Age
of Social Media: Endorsements Trump Partisan Source Affiliation When
This raises the question of how to build a recommender system that Selecting News Online. Communication Research 41(8), 1042-1063.
(1) incorporates information about profile attribute credibility [4] Dimitar Nikolov, Diego F. M. Oliveira, Alessandro Flammini and Filippo
assessments, (2) suggests useful, reliable content that users will Menczer. (2015). Measuring Online Social Bubbles. PeerJ Computer Science,
perceive as such and (3) does not reinforce unfair biases. 1(38), 12 pages.
[7] Stephan Winter, Miriam J. Metzger and Andrew J. Flanagin. (2016). Selective
Use of News Cues: A Multiple-Motive Perspective on Information Selection in
First, recent results from our work [9] and others [8] indicate a Social Media Environments. Journal of Communication, 66(4), 669-693.
preference for news shared from accounts with certain personal [8] Meredith Ringel Morris, Scott Counts, Asta Roseway, Aaron Hoff and Julia
Schwarz. (2012). Tweeting is Believing? Understanding Microblog Credibility
attributes. These preferences may vary among users. Leveraging Perceptions. In Proceedings of CSCW 2012. ACM, Seattle, Washington, 1-10.
profile attribute preferences has the potential to improve perceived [9] Brooke Auxier and Jennifer Golbeck. (2018). Factors influencing perceived
recommendation quality, but this requires empirical analysis in reliability of information-sharers in social media spaces. University of Maryland,
addition to experimental analysis. College Park, MD.
If these preferences do improve recommendation accuracy, they
will negatively impact the diversity of sources that are
recommended and potentially the diversity of news itself. To
counter this, we see research paths in countering these preferences
and in playing to them. To counter such preferences, we may look
to strong, trusted existing social connections and highlight
engagements (e.g. likes, shares) from a user’s friend who shares
content from a less-preferred profile type. This may weaken the
bias toward certain sources. When those biases may be difficult to
overcome, we are interested in the impact of bots. Recommender
systems or news organizations may consider creating automated
accounts with profiles that play to a user’s reliability bias, and have
those accounts share news that will broaden the user’s information
ecosystem.
4 Conclusion
Recommender systems help people find reliable news in an
ecosystem full of sources and perspectives. Previous results that
show users have preferences for a range of personal attributes of
news-sharing accounts, including avatar type, name, screen name,
and gender. This raises several interesting research questions.
Recommender systems may be able to improve their performance