Computational fact checking from knowledge networks

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation

Cross-language Wikipedia Editing of Okinawa, Japan

This article analyzes users who edit Wikipedia articles about Okinawa, Japan, in English and Japanese. It finds these users are among the most active and dedicated users in their primary languages, where they make many large, high-quality edits. However, when these users edit in their non-primary languages, they tend to make edits of a different type that are overall smaller in size and more often restricted to the narrow set of articles that exist in both languages. Design changes to motivate wider contributions from users in their non-primary languages and to encourage multilingual users to transfer more information across language divides are presented.Comment: In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2015. AC

Global disease monitoring and forecasting with Wikipedia

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data such as social media and search queries are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with $r^2$ up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein and adjust novelty claims accordingly; revise title; various revisions for clarit

Factors Influencing Approval of Wikipedia Bots

Before a Wikipedia bot is allowed to edit, the operator of the bot must get approval. The Bot Approvals Group (BAG), a committee of Wikipedia bot developers, users and editors, discusses each bot request to reach consensus regarding approval or denial. We examine factors related to approval of a bot by analyzing 100 bots’ project pages. The results suggest that usefulness, value-based decision making and the bot’s status (e.g., automatic or manual) are related to approval. This study may contribute to understanding decision making regarding the human-automation boundary and may lead to developing more efficient bots

Accuracy and Completeness of Drug Information in Wikipedia: A Comparison with Standard Textbooks of Pharmacology

The online resource Wikipedia is increasingly used by students for knowledge acquisition and learning. However, the lack of a formal editorial review and the heterogeneous expertise of contributors often results in skepticism by educators whether Wikipedia should be recommended to students as an information source. In this study we systematically analyzed the accuracy and completeness of drug information in the German and English language versions of Wikipedia in comparison to standard textbooks of pharmacology. In addition, references, revision history and readability were evaluated. Analysis of readability was performed using the Amstad readability index and the Erste Wiener Sachtextformel. The data on indication, mechanism of action, pharmacokinetics, adverse effects and contraindications for 100 curricular drugs were retrieved from standard German textbooks of general pharmacology and compared with the corresponding articles in the German language version of Wikipedia. Quantitative analysis revealed that accuracy of drug information in Wikipedia was 99.7%+/- 0.2% when compared to the textbook data. The overall completeness of drug information in Wikipedia was 83.8 +/- 1.5% (p<0.001). Completeness varied in-between categories, and was lowest in the category "pharmacokinetics'' (68.0% +/- 4.2%;p<0.001) and highest in the category "indication'' (91.3%+/- 2.0%) when compared to the textbook data overlap. Similar results were obtained for the English language version of Wikipedia. Of the drug information missing in Wikipedia, 62.5% was rated as didactically non-relevant in a qualitative re-evaluation study. Drug articles in Wikipedia had an average of 14.6 +/- 1.6 references and 262.8 +/- 37.4 edits performed by 142.7 +/- 17.6 editors. Both Wikipedia and textbooks samples had comparable, low readability. Our study suggests that Wikipedia is an accurate and comprehensive source of drug-related information for undergraduate medical education