The Effects of Asymmetric Social Ties, Structural Embeddedness, and Tie Strength on Online Content Contribution Behavior
Bibliographic record
Abstract
For a social media community to thrive and grow, it is critical that users of the site interact with each other and contribute content to the site. We study the role of social ties in motivating user preference expression, a form of user content contribution, in an online social media community. We examine the role of three types of ties, reciprocated, follower, and followee ties, and assess whether the structural and relational properties of a user’s social network moderate the social influence effect in user contribution. A unique disaggregate level panel data set of users’ contributions and social tie formation activities from an online music platform is employed to study the impact of social ties. To address identification issues, we adopt a quasi-experimental approach based on dynamic propensity score matching. The results provide strong evidence of the influence of online network ties in online contribution behavior. We find that the influence of reciprocated ties is the greatest, followed by influence from followee ties and then follower ties. Additional analysis reveals that reciprocated and followee ties have even greater influence when they contribute new information for a focal user. Structural embeddedness and tie strength among network ties are found to amplify the effect of social contagion in online contribution. We conduct several sensitivity and robustness checks that lend credible support to our findings. The results add to the greater understanding of social influence in online contribution and provide valuable managerial insights into designs of online communities to enable greater user participation. The online appendices are available at https://doi.org/10.1287/mnsc.2018.3087 . This paper was accepted by Anandhi Bharadwaj, information systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".