Fairness in online social network timelines: Measurements, models and mechanism design

Hargreaves, Eduardo; Agosti, Claudio; Menasché, Daniel Sadoc; Neglia, Giovanni; Reiffers-Masson, Alexandre; Altman, Eitan

doi:10.1016/j.peva.2018.09.009

Cited by 16 publications

(22 citation statements)

References 62 publications

(79 reference statements)

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“…Therefore, the difference in the VoA as predicted by the proposed model and the measurements is attributed to News Feed reordering. This finding reveals that Facebook Facebook creates a position bias [5] by showing repeated posts and is consistent with the conclusions of [17]. Note, for instance, that when K = 10 it is expected according to the proposed model that the timeline can be fulfilled with new posts, i.e.…”

Section: Experimental Results Accounting For Facebook News Feed Fisupporting

confidence: 85%

“…We refer to the selected post as the reference post of the snapshot. For further details on the behavior of FIFO timelines, we refer the reader to [17].…”

Section: A Trace-driven Fifo Simulations Without Facebook News Feed mentioning

confidence: 99%

“…Next, our goal is to assess the effect of the Facebook News Feed personalization algorithm on the VoA. For this purpose, we ran an experiment similar to the one presented in [17]. We created 5 virtual users, that will be referred to as bots.…”

Section: B Facebook Tracking Exposed (Fte) Experimental Setupmentioning

confidence: 99%

“…The dynamics of collective attention and the way that popularity of topics evolves over time was studied in [27]. Combining these dynamics with user's behaviour expressed by the sampling rate and filtering algorithms [17], [28], one can derive insights on the dynamics of topic popularity and production of information [10], particularly when there is contention for visibility and attention.…”

Section: B Attention Economy and Adsmentioning

confidence: 99%

“…TTL timelines are versatile and flexible, allowing the reordering of publications. In this paper, we build on top of [17] and [16] to account for the impact of the user's access rate, opening up new perspectives of analysis and enabling the formulation of the problem of optimal access rate of users.…”

Section: Modeling and Evaluation Of Timelines' Performancementioning

confidence: 99%

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How Often Should I Access My Online Social Networks?

Hargreaves

Menasché

Neglia³

2019

2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS

Self Cite

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Users of online social networks are faced with a conundrum of trying to be always informed without having enough time or attention budget to do so. The retention of users on online social networks has important implications, encompassing economic, psychological and infrastructure aspects. In this paper, we pose the following question: what is the optimal rate at which users should access a social network? To answer this question, we propose an analytical model to determine the value of an access (VoA) to the social network. In the simple setting considered in this paper, VoA is defined as the chance of a user accessing the network and obtaining new content. Clearly, VoA depends on the rate at which sources generate content and on the filtering imposed by the social network. Then, we pose an optimization problem wherein the utility of users grows with respect to VoA but is penalized by costs incurred to access the network. Using the proposed framework, we provide insights on the optimal access rate. Our results are parameterized using Facebook data, indicating the predictive power of the approach.

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Section: Experimental Results Accounting For Facebook News Feed Fisupporting

confidence: 85%

“…We refer to the selected post as the reference post of the snapshot. For further details on the behavior of FIFO timelines, we refer the reader to [17].…”

Section: A Trace-driven Fifo Simulations Without Facebook News Feed mentioning

confidence: 99%

Section: B Facebook Tracking Exposed (Fte) Experimental Setupmentioning

confidence: 99%

Section: B Attention Economy and Adsmentioning

confidence: 99%

Section: Modeling and Evaluation Of Timelines' Performancementioning

confidence: 99%

See 3 more Smart Citations

How Often Should I Access My Online Social Networks?

Hargreaves

Menasché

Neglia³

2019

2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS

Self Cite

View full text Add to dashboard Cite

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Neutral bots probe political bias on social media

et al. 2021

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Social media platforms attempting to curb abuse and misinformation have been accused of political bias. We deploy neutral social bots who start following different news sources on Twitter, and track them to probe distinct biases emerging from platform mechanisms versus user interactions. We find no strong or consistent evidence of political bias in the news feed. Despite this, the news and information to which U.S. Twitter users are exposed depend strongly on the political leaning of their early connections. The interactions of conservative accounts are skewed toward the right, whereas liberal accounts are exposed to moderate content shifting their experience toward the political center. Partisan accounts, especially conservative ones, tend to receive more followers and follow more automated accounts. Conservative accounts also find themselves in denser communities and are exposed to more low-credibility content.

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