Disentangling the Effects of Social Signals

By Tad Hogg, Kristina Lerman. 2014

28 citations recorded by the source corpus.

Peer recommendation is a crowdsourcing task that leverages the opinions of many to identify interesting content online, such as news, images, or videos. Peer recommendation applications often use social signals, e.g., the number of prior recommendations, to guide people to the more interesting content. How people react to social signals, in combination with content quality and its presentation order, determines the outcomes of peer recommendation, i.e., item popularity. Using Amazon Mechanical Turk, we experimentally measure the effects of social signals in peer recommendation. Specifically, after controlling for variation due to item content and its position, we find that social signals affect item popularity about half as much as position and content do. These effects are somewhat correlated, so social signals exacerbate the "rich get richer" phenomenon, which results in a wider variance of popularity. Further, social signals change individual preferences, creating a "herding" effect that biases people's judgments about the content. Despite this, we find that social signals improve the efficiency of peer recommendation by reducing the effort devoted to evaluating content while maintaining recommendation quality.

Subfields: Computer Science Applications, Information Systems, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Kristina Lerman
2. Gregory Stoddard
3. Gerardo Berbeglia
4. Andrés Abeliuk
5. Pascal Van Hentenryck
6. Tad Hogg

In Information Systems

1. Kristina Lerman
2. Gregory Stoddard
3. Gerardo Berbeglia
4. Andrés Abeliuk
5. Pascal Van Hentenryck
6. Tad Hogg

In Statistical and Nonlinear Physics

1. Kristina Lerman
2. Gregory Stoddard
3. Tad Hogg
4. Gerardo Berbeglia
5. Andrés Abeliuk
6. Pascal Van Hentenryck
7. Nazanin Alipourfard
8. Peter G. Fennell

Citations in the corpus

• The myopia of crowds: Cognitive load and collective evaluation of answers on Stack Exchange (2016, unknown): “Position bias, or rank order, plays a large effect in user choices even after accounting for item quality [14, 21], which is in agreement with the results presented here.”
• Popularity and Quality in Social News Aggregators: A Study of Reddit and Hacker News (2015, background): “In follow-up work, Hogg and Lerman (2014) use the same experiment to test the effect of social influence.”
• Taming the Unpredictability of Cultural Markets with Social Influence (2017, background): “In fact, they found that “social influence affects popularity about half as much as position and content do” [13].”
• Information is not a Virus, and Other Consequences of Human Cognitive Limits (2016, background): “Social influence bias, communicated through social signals, helps direct attention to online content that has been liked, shared or approved by many others [34, 35].”