The Detection of Emerging Trends Using Wikipedia Traffic Data and Context Networks

Changes in Reproductive Health Information-Seeking Behaviors After the Dobbs Decision: Systematic Search of the Wikimedia Database

BACKGROUND

After the US Supreme Court overturned Roe v. Wade, confusion followed regarding the legality of abortion in different states across the country. Recent studies found increased Google searches for abortion-related terms in restricted states after the Dobbsv. Jackson Women's Health Organization decision was leaked. As patients and providers use Wikipedia (Wikimedia Foundation) as a predominant medical information source, we hypothesized that changes in reproductive health information-seeking behavior could be better understood by examining Wikipedia article traffic.

OBJECTIVE

This study aimed to examine trends in Wikipedia usage for abortion and contraception information before and after the Dobbs decision.

METHODS

Page views of abortion- and contraception-related Wikipedia pages were scraped. Temporal changes in page views before and after the Dobbs decision were then analyzed to explore changes in baseline views, differences in views for abortion-related information in states with restrictive abortion laws versus nonrestrictive states, and viewer trends on contraception-related pages.

RESULTS

Wikipedia articles related to abortion topics had significantly increased page views following the leaked and final Dobbs decision. There was a 103-fold increase in the page views for the Wikipedia article Roe v. Wade following the Dobbs decision leak (mean 372,654, SD 135,478 vs mean 3614, SD 248; P<.001) and a 67-fold increase in page views following the release of the final Dobbs decision (mean 8942, SD 402 vs mean 595,871, SD 178,649; P<.001). Articles about abortion in the most restrictive states had a greater increase in page views (mean 40.6, SD 12.7; 18/51, 35% states) than articles about abortion in states with some restrictions or protections (mean 26.8, SD 7.3; 24/51, 47% states; P<.001) and in the most protective states (mean 20.6, SD 5.7; 8/51, 16% states; P<.001). Finally, views to pages about common contraceptive methods significantly increased after the Dobbs decision. "Vasectomy" page views increased by 183% (P<.001), "IUD" (intrauterine device) page views increased by 80% (P<.001), "Combined oral contraceptive pill" page views increased by 24% (P<.001), "Emergency Contraception" page views increased by 224% (P<.001), and "Tubal ligation" page views increased by 92% (P<.001).

CONCLUSIONS

People sought information on Wikipedia about abortion and contraception at increased rates after the Dobbs decision. Increased traffic to abortion-related Wikipedia articles correlated to the restrictiveness of state abortion policies. Increased interest in contraception-related pages reflects the increased demand for contraceptives observed after the Dobbs decision. Our work positions Wikipedia as an important source of reproductive health information and demands increased attention to maintain and improve Wikipedia as a reliable source of health information after the Dobbs decision.

Wikipedia as a tool for contemporary history of science: A case study on CRISPR

Rapid developments and methodological divides hinder the study of how scientific knowledge accumulates, consolidates and transfers to the public sphere. Our work proposes using Wikipedia, the online encyclopedia, as a historiographical source for contemporary science. We chose the high-profile field of gene editing as our test case, performing a historical analysis of the English-language Wikipedia articles on CRISPR. Using a mixed-method approach, we qualitatively and quantitatively analyzed the CRISPR article's text, sections and references, alongside 50 affiliated articles. These, we found, documented the CRISPR field's maturation from a fundamental scientific discovery to a biotechnological revolution with vast social and cultural implications. We developed automated tools to support such research and demonstrated its applicability to two other scientific fields-coronavirus and circadian clocks. Our method utilizes Wikipedia as a digital and free archive, showing it can document the incremental growth of knowledge and the manner scientific research accumulates and translates into public discourse. Using Wikipedia in this manner compliments and overcomes some issues with contemporary histories and can also augment existing bibliometric research.

Measuring user interactions with websites: A comparison of two industry standard analytics approaches using data of 86 websites

This research compares four standard analytics metrics from Google Analytics with SimilarWeb using one year’s average monthly data for 86 websites from 26 countries and 19 industry verticals. The results show statistically significant differences between the two services for total visits, unique visitors, bounce rates, and average session duration. Using Google Analytics as the baseline, SimilarWeb average values were 19.4% lower for total visits, 38.7% lower for unique visitors, 25.2% higher for bounce rate, and 56.2% higher for session duration. The website rankings between SimilarWeb and Google Analytics for all metrics are significantly correlated, especially for total visits and unique visitors. The accuracy/inaccuracy of the metrics from both services is discussed from the vantage of the data collection methods employed. In the absence of a gold standard, combining the two services is a reasonable approach, with Google Analytics for onsite and SimilarWeb for network metrics. Finally, the differences between SimilarWeb and Google Analytics measures are systematic, so with Google Analytics metrics from a known site, one can reasonably generate the Google Analytics metrics for related sites based on the SimilarWeb values. The implications are that SimilarWeb provides conservative analytics in terms of visits and visitors relative to those of Google Analytics, and both tools can be utilized in a complementary fashion in situations where site analytics is not available for competitive intelligence and benchmarking analysis.

Using agricultural metadata: a novel investigation of trends in sowing date in on-farm research trials using the Online Farm Trials database

Background: A growing ability to collect data, together with the development and adoption of the FAIR guiding principles, has increased the amount of data available in many disciplines. This has given rise to an urgent need for robust metadata. Within the Australian grains industry, data from thousands of on-farm research trials (Trial Projects) have been made available via the Online Farm Trials (OFT) website. OFT Trial Project metadata were developed as filters to refine front-end database searches, but could also be used as a dataset to investigate trends in metadata elements. Australian grains crops are being sown earlier, but whether on-farm research trials reflect this change is currently unknown.

Methods: We investigated whether OFT Trial Project metadata could be used to detect trends in sowing dates of on-farm crop research trials across Australia, testing the hypothesis that research trials are being sown earlier in line with local farming practices. The investigation included 15 autumn-sown, winter crop species listed in the database, with trial records from 1993 to 2019.

Results: Our analyses showed that (i) OFT Trial Project metadata can be used as a dataset to detect trends in sowing date; and (ii) cropping research trials are being sown earlier in Victoria and Western Australia, but no trend exists within the other states.

Discussion/Conclusion: Our findings show that OFT Trial Project metadata can be used to detect trends in crop sowing date, suggesting that metadata could also be used to detect trends in other elements such as harvest date. Because OFT is a national database of research trials, further assessment of metadata may uncover important agronomic, cultural or economic trends within or across the Australian cropping regions. New information could then be used to lead practice change and increase productivity within the Australian grains industry.

Content Growth and Attention Contagion in Information Networks: Addressing Information Poverty on Wikipedia

Open collaboration platforms have fundamentally changed the way that knowledge is produced, disseminated, and consumed. Although the community governance and open collaboration model of Wikipedia confers many benefits, its decentralized nature can leave questions of information poverty and skewness to the mercy of the system's natural dynamics. In this paper, we leverage a large-scale natural experiment to gain a causal understanding of how exogenous content contributions to Wikipedia articles affect the attention that they attract and how that attention spills over to other articles in the information network. We find a positive feedback loop: content contribution leads to significant and long-lasting increases of attention and future contribution. Unfortunately, this also suggests that impoverished regions of information networks are likely to remain so in the absence of intervention. However, our analysis reveals a potential solution. Articles in impoverished regions of information networks are particularly positioned to benefit from the phenomenon of attention spillovers. Using a simulation that is calibrated with real-world link traffic of the Wikipedia network, we show that an attention contagion policy, which focuses editorial effort coherently on impoverished regions, can lead to as much as a twofold gain in attention relative to unguided contributions.

Analysis of Data Persistence in Collaborative Content Creation Systems: The Wikipedia Case

A very common problem in designing caching/prefetching systems, distribution networks, search engines, and web-crawlers is determining how long a given content lasts before being updated, i.e., its update frequency. Indeed, while some content is not frequently updated (e.g., videos), in other cases revisions periodically invalidate contents. In this work, we present an analysis of Wikipedia, currently the 5th most visited website in the world, evaluating the statistics of updates of its pages and their relationship with page view statistics. We discovered that the number of updates of a page follows a lognormal distribution. We provide fitting parameters as well as a goodness of fit analysis, showing the statistical significance of the model to describe the empirical data. We perform an analysis of the views–updates relationship, showing that in a time period of a month, there is a lack of evident correlation between the most updated pages and the most viewed pages. However, observing specific pages, we show that there is a strong correlation between the peaks of views and updates, and we find that in more than 50% of cases, the time difference between the two peaks is less than a week. This reflects the underlying process whereby an event causes both an update and a visit peak that occurs with different time delays. This behavior can pave the way for predictive traffic analysis applications based on content update statistics. Finally, we show how the model can be used to evaluate the performance of an in-network caching scenario.

Multiscale Entropy Analysis of Page Views: A Case Study of Wikipedia

In this study, the Wikipedia page views for four selected topics, namely, education, the economy/finance, medicine, and nature/environment from 2016–2018 are collected and the sample entropies of the three years’ page views are estimated and investigated using a short-time series multiscale entropy (sMSE) algorithm for a comprehensible understanding of the complexity of human website searching activities. The sample entropies of the selected topics are found to exhibit different temporal variations. In the past three years, the temporal characteristics of the sample entropies are vividly revealed, and the sample entropies of the selected topics follow the same tendencies and can be quantitatively ranked. By taking the 95% confidence interval into account, the temporal variations of sample entropies are further validated by statistical analysis (non-parametric), including the Wilcoxon signed-rank test and the Mann-Whitney U-test. The results suggest that the sample entropies estimated by the sMSE algorithm are feasible for analyzing the temporal variations of complexity for certain topics, whereas the regular variations of estimated sample entropies of different selected topics can’t simply be accepted as is. Potential explanations and paths in forthcoming studies are also described and discussed.

Rise to fame: events, media activity and public interest in pangolins and pangolin trade, 2005–2016

Attention focused on pangolins (Pholidota) and the threats posed to their survival and welfare by international trade (for use in Chinese Traditional Medicine and the Asian wild meat market) has skyrocketed across all digital information platforms over the last decade. Previously obscure and often referred to as the ‘mammal you’ve never heard of’, pangolins are now widely recognised as an icon of the illegal wildlife trade. We document the events that led to the pangolins’ ‘rise to fame’, culminating in its Appendix I listing by CITES in September 2016 and a global commercial trade ban and explore temporal co-occurrence between events and peaks in media activity and public interest with the aim of identifying events (or types of events) that may have been influential in terms of awareness-raising. More broadly, our objective was to highlight lessons in public communication that might be applied to awareness campaigns for other lesser-known threatened species. We found no evidence that any particular type of event was more likely to generate a significant media/public response than any other, but peaks in public interest co-occurred with reports of pangolin seizures, highlighting the importance of news coverage of these incidents. Further, although neither editorial nor social media peaks were strongly correlated with the timing of events, they sometimes co-occurred with different events and each differed in their coverage of different types of events, suggesting that editorial and social media have independent and distinct roles to play in conservation communication. However, despite their iconic status, public interest in pangolins is still not equivalent to that directed at, for example, tigers, elephants or lions, so efforts need to be sustained. Finally, we note that, although attention can help to generate funds and influence policy, this alone will not be enough to achieve a favourable conservation status for pangolins – on-going and future work need to ensure that public enthusiasm for this species is translated into effective protection.

Measuring Global Disease with Wikipedia: Success, Failure, and a Research Agenda

Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.