Competitive vs. Complementary Effects in Online Social Networks and News Consumption: A Natural Experiment
Bibliographic record
Abstract
Using hourly traffic and readership data from a major news website, and taking advantage of a global Facebook outage, we study the relationship between social networks and online news consumption. More specifically, we test if online social networks compete with content providers or instead play a complementary role by promoting and attracting traffic to external websites. During the outage, consistent with a promotional effect, we observe a significant decrease in traffic and unique visitors to the news website lasting beyond the outage hours. We further find that direct referrals from Facebook links grossly underestimated the actual impact of Facebook in generating traffic. Instead, during the outage, we observe a more significant reduction in visitors arriving at the news website from search engines or directly typing the website URL or using bookmarks. Additionally, readership of articles and types of pages viewed also changed during the outage. Although we observe a drop in news consumption during the outage hours for all news categories, the subsequent news consumption differs across categories. Time sensitive categories like sports and local news see an increase in consumption, whereas news on women issues or health topics see a decrease. Analysis of individual-level visit and readership behavior during the outage also reveals that Facebook not only introduces selectivity bias by attracting shallower readers but also changes readership patterns (in the absence of Facebook, visitors engage in more in-depth reading). To test the generalizability of our results, we study the impact of the outage on referrals from other social media outlets, on other news sites, and on other content and e-commerce sites. We find similar effects on other news providers, whereas data from nonnews sites, including e-commerce, show no major outage effects. Overall, our results have important managerial implications. We highlight how our results unearth the importance of search engine optimization and of strong branding for news websites, if providers want to harness fully the power of their social media presence. This paper was accepted by Chris Forman, 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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".