Does clustered presentation lead readers to diverse selections?

Chhabra, Sidharth; Resnick, Paul

doi:10.1145/2468356.2468659

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…Even when readers become aware of content with a political slant opposed to their own, they may lack the motivation to consume that content that due to political polarization and confirmation-seeking tendencies [6,13,19,27,43,55,73]. Indeed, some research has found that while people might read more content when using diverse content selection tools, this leads primarily to an increase in the amount of content consumed, not the diversity of the content [14]. One reason for this outcome could be individual differences between diversityseeking and challenge-averse people; challenge-averse people may tend not to consume diverse content [53].…”

Section: Barriers To Multiperspective News Consumption Onlinementioning

confidence: 99%

“…Example Response Perspectives and Biases (22) They were taking different sides of the equation and putting forward different thought processes Information Placement or Depth (14) The right article was less descriptive and focused more on the restrictions and not the case. It was definitely telling the story from one point of view.…”

Section: Theme(n)mentioning

confidence: 99%

“…Therefore, developing an informed opinion on a subject requires critically consuming news content from multiple sources. While the internet gives users access to news from multiple sources, when given choices, people tend to choose content that aligns with their viewpoints due to confirmation-seeking tendencies [14,55,55,70,73]. Furthermore, the task of engaging with multiple perspectives is not easy and probably not performed equitably by all users [34,66].…”

Section: Introductionmentioning

confidence: 99%

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NewsComp: Facilitating Diverse News Reading through Comparative Annotation

Bhuiyan,

Lee,

Goyal

et al. 2023

Preprint

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To support efficient, balanced news consumption, merging articles from diverse sources into one, potentially through crowdsourcing, could alleviate some hurdles. However, the merging process could also impact annotators' attitudes towards the content. To test this theory, we propose comparative news annotation; that is, annotating similarities and differences between a pair of articles. By developing and deploying NewsComp-a prototype system-we conducted a between-subjects experiment (N = 109) to examine how users' annotations compare to experts', and how comparative annotation affects users' perceptions of article credibility and quality. We found that comparative annotation can marginally impact users' credibility perceptions in certain cases; it did not impact perceptions of quality. While users' annotations were not on par with experts', they showed greater precision in finding similarities than in identifying disparate important statements. The comparison process also led users to notice differences in information placement and depth, degree of factuality/opinion, and empathetic/inflammatory language use. We discuss implications for the design of future comparative annotation tasks. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Empirical studies in HCI .

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Section: Barriers To Multiperspective News Consumption Onlinementioning

confidence: 99%

Section: Theme(n)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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NewsComp: Facilitating Diverse News Reading through Comparative Annotation

Bhuiyan,

Lee,

Goyal

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…To use only LDA topics for diversification, we set α = 1 and β = 0 in Equation 4. The corresponding distance function is given below:…”

Section: Comparing Lda and Entitiesmentioning

confidence: 99%

“…Apart from reducing monotony in the recommendation, diversification also helps in having better coverage of items in the suggested list, increasing novelty and serendipity, managing uncertainty and risk in recommendation etc. [2], [3], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Diversification in news recommendation for privacy concerned users

Desarkar

Shinde

2014

2014 International Conference on Data Science and Advanced Analytics (DSAA)

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With more and more news sources publishing news articles online, news recommendation systems are becoming increasingly popular in recent days. However, if a news recommender shows to the user many news articles that cover same story or talk about same persons or events, then the recommendations might become monotonous for the user. A possible way to handle this scenario is to diversify the recommended news articles so that the articles in the recommendation list are not too similar to each other. Existing research works in literature discuss how to use user-specific relevance of news articles and diversification to produce the final recommendations. To be able to compute user-specific relevance of articles, several recommender systems suggest that users have a login, or they set cookies to track page visits of the users. Often users are unsure whether the information collected via these means will be used for the sole purpose of news recommendation. Privacy of the identity and page access patterns is a concern for many users. In this work, we consider the problem where diverse recommendations are to be given for users, but no past data about users is collected or stored by the system. We use only article popularity to estimate the relevance. We identify few aspects of news articles on which diversification can be performed. These aspects are incorporated in an optimization framework. An approximation algorithm is used to generate the recommendations. Experimental analysis shows that in scenarios where no individual user data is stored or accessible by the recommender system, diversification results in higher click rates and increase in the number of satisfied users. Performances of the multiple diversification aspects and approaches are also analyzed.

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Data Portraits and Intermediary Topics

Graells-Garrido¹,

Lalmas

Baeza-Yates

2016

Proceedings of the 21st International Conference on Intelligent User Interfaces

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In micro-blogging platforms, people connect and interact with others. However, due to cognitive biases, they tend to interact with like-minded people and read agreeable information only. Many efforts to make people connect with those who think differently have not worked well. In this paper, we hypothesize, first, that previous approaches have not worked because they have been direct -they have tried to explicitly connect people with those having opposing views on sensitive issues. Second, that neither recommendation or presentation of information by themselves are enough to encourage behavioral change. We propose a platform that mixes a recommender algorithm and a visualization-based user interface to explore recommendations. It recommends politically diverse profiles in terms of distance of latent topics, and displays those recommendations in a visual representation of each user's personal content. We performed an "in the wild" evaluation of this platform, and found that people explored more recommendations when using a biased algorithm instead of ours. In line with our hypothesis, we also found that the mixture of our recommender algorithm and our user interface, allowed politically interested users to exhibit an unbiased exploration of the recommended profiles. Finally, our results contribute insights in two aspects: first, which individual differences are important when designing platforms aimed at behavioral change; and second, which algorithms and user interfaces should be mixed to help users avoid cognitive mechanisms that lead to biased behavior.

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Does clustered presentation lead readers to diverse selections?

Cited by 5 publications

References 3 publications

NewsComp: Facilitating Diverse News Reading through Comparative Annotation

NewsComp: Facilitating Diverse News Reading through Comparative Annotation

Diversification in news recommendation for privacy concerned users

Data Portraits and Intermediary Topics

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