Motivation

Social media platforms centered around content creators (CCs) faced rapid growth in the past decade. Currently, millions of CCs make livable incomes through platforms such as YouTube, TikTok, and Instagram. As such, similarly to the job market, it is important to ensure the success and income (usually related to the follower counts) of CCs reflect the quality of their work. Since quality cannot be observed directly, two other factors govern the network-formation process: (a) the visibility of CCs (resulted from, e.g., recommender systems and moderation processes) and (b) the decision-making process of seekers (i.e., of users focused on finding CCs). Prior virtual experiments and empirical work seem contradictory regarding fairness: While the first suggests the expected number of followers of CCs reflects their quality, the second says that quality does not perfectly predict success.

Results

Our paper extends prior models in order to bridge this gap between theoretical and empirical work. We (a) define a parameterized recommendation process which allocates visibility based on popularity biases, (b) define two metrics of individual fairness (ex-ante and ex-post), and (c) define a metric for seeker satisfaction. Through an analytical approach we show our process is an absorbing Markov Chain where exploring only the most popular CCs leads to lower expected times to absorption but higher chances of unfairness for CCs. While increasing the exploration helps, doing so only guarantees fair outcomes for the highest (and lowest) quality CC. Simulations revealed that CCs and seekers prefer different algorithmic designs: CCs generally have higher chances of fairness with anti-popularity biased recommendation processes, while seekers are more satisfied with popularity-biased recommendations. Altogether, our results suggest that while the exploration of low-popularity CCs is needed to improve fairness, platforms might not have the incentive to do so and such interventions do not entirely prevent unfair outcomes.

中文翻译：

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运气在社交媒体影响者成功中的作用

动机

以内容创作者（CC）为中心的社交媒体平台在过去十年中经历了快速增长。目前，数以百万计的CC通过YouTube、TikTok和Instagram等平台赚取维持生计的收入。因此，与就业市场类似，确保 CC 的成功和收入（通常与关注者数量相关）反映其工作质量非常重要。由于质量无法直接观察到，因此另外两个因素控制着网络形成过程：(a) CC 的可见性（例如来自推荐系统和审核过程）和 (b)搜索者的决策过程（即，专注于寻找 CC 的用户）。先前的虚拟实验和实证工作在公平性方面似乎是矛盾的：虽然第一个建议 CC 的预期追随者数量反映了他们的质量，但第二个建议质量并不能完美预测成功。

结果

我们的论文扩展了先前的模型，以弥合理论和实证工作之间的差距。我们（a）定义一个参数化推荐过程，根据受欢迎程度偏差分配可见性，（b）定义个人公平性的两个指标（事前和事后），以及（c）定义搜索者满意度的指标。通过分析方法，我们表明我们的过程是一个吸收性马尔可夫链，其中仅探索最受欢迎的 CC 会导致吸收的预期时间较短，但 CC 不公平的可能性较高。虽然增加探索有所帮助，但这样做只能保证最高（和最低）质量 CC 的公平结果。模拟显示，CC 和搜索者更喜欢不同的算法设计：CC 通常在反受欢迎程度偏向的推荐过程中具有更高的公平机会，而搜索者对受欢迎程度偏向的推荐更满意。总而言之，我们的结果表明，虽然需要探索低受欢迎的 CC 来提高公平性，但平台可能没有动力这样做，而且此类干预措施并不能完全防止不公平的结果。