scispace - formally typeset
Proceedings ArticleDOI

Feed me: motivating newcomer contribution in social network sites

Reads0
Chats0
TLDR
This work uses server log data from approximately 140,000 newcomers in Facebook to predict long-term sharing based on the experiences the newcomers have in their first two weeks, and finds support for social learning: newcomers who see their friends contributing go on to share more content themselves.
Abstract
Social networking sites (SNS) are only as good as the content their users share. Therefore, designers of SNS seek to improve the overall user experience by encouraging members to contribute more content. However, user motivations for contribution in SNS are not well understood. This is particularly true for newcomers, who may not recognize the value of contribution. Using server log data from approximately 140,000 newcomers in Facebook, we predict long-term sharing based on the experiences the newcomers have in their first two weeks. We test four mechanisms: social learning, singling out, feedback, and distribution. In particular, we find support for social learning: newcomers who see their friends contributing go on to share more content themselves. For newcomers who are initially inclined to contribute, receiving feedback and having a wide audience are also predictors of increased sharing. On the other hand, singling out appears to affect only those newcomers who are not initially inclined to share. The paper concludes with design implications for motivating newcomer sharing in online communities.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

A Review of Facebook Research in the Social Sciences

TL;DR: The authors conducted a comprehensive literature search, identifying 412 relevant articles, which were sorted into 5 categories: descriptive analysis of users, motivations for using Facebook, identity presentation, the role of Facebook in social interactions, and privacy and information disclosure.
Journal ArticleDOI

News sharing in social media: The effect of gratifications and prior experience

TL;DR: Results from structural equation modeling (SEM) analysis revealed that respondents who were driven by gratifications of information seeking, socializing, entertainment, status seeking, and status seeking were more likely to share news in social media platforms.
Proceedings ArticleDOI

Characterizing user behavior in online social networks

TL;DR: A first of a kind analysis of user workloads in online social networks, based on detailed clickstream data collected over a 12-day period, shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities.
Proceedings ArticleDOI

Is it really about me?: message content in social awareness streams

TL;DR: A content-based categorization of the type of messages posted by Twitter users is developed, based on which the analysis shows two common types of user behavior in terms of the content of the posted messages, and exposes differences between users in respect to these activities.
Proceedings ArticleDOI

Steering user behavior with badges

TL;DR: A formal model for reasoning about user behavior in the presence of badges is introduced and several robust design principles emerge from the framework that could potentially aid in the design of incentives for a broad range of sites.
References
More filters
Journal ArticleDOI

Sample Selection Bias as a Specification Error

James J. Heckman
- 01 Jan 1979 - 
TL;DR: In this article, the bias that results from using non-randomly selected samples to estimate behavioral relationships as an ordinary specification error or "omitted variables" bias is discussed, and the asymptotic distribution of the estimator is derived.
Journal ArticleDOI

Social learning theory

TL;DR: In this article, an exploración de the avances contemporaneos en la teoria del aprendizaje social, con especial enfasis en los importantes roles que cumplen los procesos cognitivos, indirectos, and autoregulatorios.
Journal ArticleDOI

The need to belong: Desire for interpersonal attachments as a fundamental human motivation.

TL;DR: Existing evidence supports the hypothesis that the need to belong is a powerful, fundamental, and extremely pervasive motivation, and people form social attachments readily under most conditions and resist the dissolution of existing bonds.
Journal ArticleDOI

A Theory of Social Comparison Processes

Leon Festinger
- 01 May 1954 - 
TL;DR: In this article, the authors pointed out that there is a strong functional tie between opinions and abilities in humans and that the ability evaluation of an individual can be expressed as a comparison of the performance of a particular ability with other abilities.
Related Papers (5)