The affordances of social media and the internet allow users to encounter and engage with diverse and novel news content. However, user preference and bias may limit the content consumed in these spaces. Using signals of reliability, which have been studied as they relate to content and information sharers in social media environments, a recommender system could be built to suggest news content to users. However, as stated, assessments of trustworthiness and reliability come with some user bias and an algorithm that uses these preferences could be extremely limiting. We propose the following solutions: (1) using trusted social connections to surface content; (2) using bots to broaden a user's information ecosystem.
Billions of people across the globe use social media sites. In 2016,
2.28 billion people were on social media and that number is
expected to increase to 3.02 billion by 2021 [
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
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
[
Audiences also judge the information sharers themselves. Users
that had a default image, cartoon or avatar as their profile photos
were rated as having low credibility, whereas users with high
follower counts who had a Twitter bio were seen as more credible
[
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, an algorithm could be designed to bring more content from those types of accounts.
However, one can see that there is some bias in these evaluations
of trustworthiness and credibility. There are plenty of reliable,
trustworthy and talented journalists and who do not have Western
sounding names and who use cartoon avatars in their profile photos
- though subjects in our study gave these types of users among the
lowest credibility ratings. There are also many nefarious sources
with profiles that match the traditional standards of trustworthiness.
This raises the question of how to build a recommender system that
(1) incorporates information about profile attribute credibility
assessments, (2) suggests useful, reliable content that users will
perceive as such and (3) does not reinforce unfair biases.
First, recent results from our work [
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
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 by incorporating profile attribute preferences. This would need experimental validation. However, any improved performance may come at the cost of bias in the type of sources and in the diversity of news a person sees. Countering user bias through recommendation and leveraging the perceived reliability of a 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 users assess people they encounter online.