Brands as personal narratives: learning from user–YouTube–brand interactions

Sashittal, Hemant C.; Jassawalla, Avan R.

doi:10.1057/s41262-021-00248-4

Cited by 6 publications

(2 citation statements)

References 110 publications

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“…This has been associated with major societal impacts [2], triggering a debate on whether or not the personalization brings users on radicalization paths [3,4]. To a less extreme end, the problem of users getting a narrow view on the possibilities offered by the platforms has been studied qualitatively under the terms of filter bubbles [5][6][7], and more recently the rabbit-hole (shorthanded RH through the paper) phenomenon [4,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Modeling rabbit-holes on YouTube

Merrer

Trédan

Yesilkanat

2023

Soc. Netw. Anal. Min.

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Numerous discussions have advocated the presence of a so called rabbit-hole (RH) phenomenon on social media, interested in advanced personalization to their users. This phenomenon is loosely understood as a collapse of mainstream recommendations, in favor of ultra personalized ones that lock users into narrow and specialized feeds. Yet quantitative studies are often ignoring personalization, are of limited scale, and rely on manual tagging to track this collapse. This precludes a precise understanding of the phenomenon based on reproducible observations, and thus the continuous audits of platforms. In this paper, we first tackle the scale issue by proposing a user-sided bot-centric approach that enables large scale data collection, through autoplay walks on recommendations. We then propose a simple theory that explains the appearance of these RHs. While this theory is a simplifying viewpoint on a complex and planetwide phenomenon, it carries multiple advantages: it can be analytically modeled, and provides a general yet rigorous definition of RHs. We define them as an interplay between i) user interaction with personalization and ii) the attraction strength of certain video categories, which cause users to quickly step apart of mainstream recommendations made to fresh user profiles. We illustrate these concepts by highlighting some RHs found after collecting more than 16 million personalized recommendations on YouTube. A final validation step compares our automatically-identified RHs against manually-identified RHs from a previous research work. Together, those results pave the way for large scale and automated audits of the RH effect in recommendation systems.

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Section: Introductionmentioning

confidence: 99%

Modeling rabbit-holes on YouTube

Merrer

Trédan

Yesilkanat

2023

Soc. Netw. Anal. Min.

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“…They have the capacity and capability to produce content on any topic or category. However, not all content posted on video platforms such as YouTube gets the desired attention, and only a fraction can reach a large audience, particularly the videos posted by social media marketers expecting millions of views [4][5][6][7][8]. Companies want to identify these categories with the highest potential for a high number of views on the platform, thereby maximizing profit while considering parameters affecting the quality and timeliness of video production and publishing.…”

Section: Introductionmentioning

confidence: 99%

AHP Approach for Determining Category in Social Media Content Creation in Order to Maximize Revenue per Mille (RPM)

Guzman

Robielos

2022

HighTech. Innov. J.

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This study utilizes the Analytic Hierarchy Process (AHP) in the selection of an optimal niche or category of videos for maximizing view count. The main income from videos is derived from RPM, which is a set amount per thousand views. A set of criteria was determined from attributes in the dataset that logically contribute to either the videos’ SEO or trend/popularity. The criteria in question were also determined by commonalities across a vast number of video content platforms, which focus more on the essential attributes of a video. In order to perform pairwise comparison, weights were derived from coefficients generated using Linear Regression. Following the creation of the model, we identify the categories with the highest potential for gaining views. Based on the results, the study may be performed in another time frame to reflect the major shifts in public interest over time. Thus, the importance of its repeatability and degree of usability across datasets from different platforms is emphasized. Doi: 10.28991/HIJ-2022-03-01-07 Full Text: PDF

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