Building automated vandalism detection tools for Wikidata

Wikidata, like Wikipedia, is a knowledge base that anyone can edit. This open collaboration model is powerful in that it reduces barriers to participation and allows a large number of people to contribute. However, it exposes the knowledge base to the risk of vandalism and low-quality contributions. In this work, we build on past work detecting vandalism in Wikipedia to detect vandalism in Wikidata. This work is novel in that identifying damaging changes in a structured knowledge-base requires substantially different feature engineering work than in a text-based wiki like Wikipedia. We also discuss the utility of these classifiers for reducing the overall workload of vandalism patrollers in Wikidata. We describe a machine classification strategy that is able to catch 89% of vandalism while reducing patrollers' workload by 98%, by drawing lightly from contextual features of an edit and heavily from the characteristics of the user making the edit

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Neural Based Statement Classification for Biased Language

Biased language commonly occurs around topics which are of controversial nature, thus, stirring disagreement between the different involved parties of a discussion. This is due to the fact that for language and its use, specifically, the understanding and use of phrases, the stances are cohesive within the particular groups. However, such cohesiveness does not hold across groups. In collaborative environments or environments where impartial language is desired (e.g. Wikipedia, news media), statements and the language therein should represent equally the involved parties and be neutrally phrased. Biased language is introduced through the presence of inflammatory words or phrases, or statements that may be incorrect or one-sided, thus violating such consensus. In this work, we focus on the specific case of phrasing bias, which may be introduced through specific inflammatory words or phrases in a statement. For this purpose, we propose an approach that relies on a recurrent neural networks in order to capture the inter-dependencies between words in a phrase that introduced bias. We perform a thorough experimental evaluation, where we show the advantages of a neural based approach over competitors that rely on word lexicons and other hand-crafted features in detecting biased language. We are able to distinguish biased statements with a precision of P=0.92, thus significantly outperforming baseline models with an improvement of over 30%. Finally, we release the largest corpus of statements annotated for biased language.Comment: The Twelfth ACM International Conference on Web Search and Data Mining, February 11--15, 2019, Melbourne, VIC, Australi

Wikipedia vandalism detection: combining natural language, metadata, and reputation features

Wikipedia is an online encyclopedia which anyone can edit. While most edits are constructive, about 7% are acts of vandalism. Such behavior is characterized by modifications made in bad faith; introducing spam and other inappropriate content. In this work, we present the results of an effort to integrate three of the leading approaches to Wikipedia vandalism detection: a spatio-temporal analysis of metadata (STiki), a reputation-based system (WikiTrust), and natural language processing features. The performance of the resulting joint system improves the state-of-the-art from all previous methods and establishes a new baseline for Wikipedia vandalism detection. We examine in detail the contribution of the three approaches, both for the task of discovering fresh vandalism, and for the task of locating vandalism in the complete set of Wikipedia revisions.The authors from Universitat Politècnica de València thank also the MICINN research project TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 (Plan I+D+i). UPenn contributions were supported in part by ONR MURI N00014-07-1-0907. This research was partially supported by award 1R01GM089820-01A1 from the National Institute Of General Medical Sciences, and by ISSDM, a UCSC-LANL educational collaboration.Adler, BT.; Alfaro, LD.; Mola Velasco, SM.; Rosso, P.; West, AG. (2011). Wikipedia vandalism detection: combining natural language, metadata, and reputation features. En Computational Linguistics and Intelligent Text Processing. Springer Verlag (Germany). 6609:277-288. https://doi.org/10.1007/978-3-642-19437-5_23S2772886609Wikimedia Foundation: Wikipedia (2010) [Online; accessed December 29, 2010]Wikimedia Foundation: Wikistats (2010) [Online; accessed December 29, 2010]Potthast, M.: Crowdsourcing a Wikipedia Vandalism Corpus. In: Proc. of the 33rd Intl. ACM SIGIR Conf. (SIGIR 2010). ACM Press, New York (July 2010)Gralla, P.: U.S. senator: It’s time to ban Wikipedia in schools, libraries, http://blogs.computerworld.com/4598/u_s_senator_its_time_to_ban_wikipedia_in_schools_libraries [Online; accessed November 15, 2010]Olanoff, L.: School officials unite in banning Wikipedia. Seattle Times (November 2007)Mola-Velasco, S.M.: Wikipedia Vandalism Detection Through Machine Learning: Feature Review and New Proposals. In: Braschler, M., Harman, D. (eds.) Notebook Papers of CLEF 2010 LABs and Workshops, Padua, Italy, September 22-23 (2010)Adler, B., de Alfaro, L., Pye, I.: Detecting Wikipedia Vandalism using WikiTrust. In: Braschler, M., Harman, D. (eds.) Notebook Papers of CLEF 2010 LABs and Workshops, Padua, Italy, September 22-23 (2010)West, A.G., Kannan, S., Lee, I.: Detecting Wikipedia Vandalism via Spatio-Temporal Analysis of Revision Metadata. In: EUROSEC 2010: Proceedings of the Third European Workshop on System Security, pp. 22–28 (2010)West, A.G.: STiki: A Vandalism Detection Tool for Wikipedia (2010), http://en.wikipedia.org/wiki/Wikipedia:STikiWikipedia: User: AntiVandalBot – Wikipedia, http://en.wikipedia.org/wiki/User:AntiVandalBot (2010) [Online; accessed November 2, 2010]Wikipedia: User:MartinBot – Wikipedia (2010), http://en.wikipedia.org/wiki/User:MartinBot [Online; accessed November 2, 2010]Wikipedia: User:ClueBot – Wikipedia (2010), http://en.wikipedia.org/wiki/User:ClueBot [Online; accessed November 2, 2010]Carter, J.: ClueBot and Vandalism on Wikipedia (2008), http://www.acm.uiuc.edu/~carter11/ClueBot.pdf [Online; accessed November 2, 2010]Rodríguez Posada, E.J.: AVBOT: detección y corrección de vandalismos en Wikipedia. NovATIca (203), 51–53 (2010)Potthast, M., Stein, B., Gerling, R.: Automatic Vandalism Detection in Wikipedia. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 663–668. Springer, Heidelberg (2008)Smets, K., Goethals, B., Verdonk, B.: Automatic Vandalism Detection in Wikipedia: Towards a Machine Learning Approach. In: WikiAI 2008: Proceedings of the Workshop on Wikipedia and Artificial Intelligence: An Evolving Synergy, pp. 43–48. AAAI Press, Menlo Park (2008)Druck, G., Miklau, G., McCallum, A.: Learning to Predict the Quality of Contributions to Wikipedia. In: WikiAI 2008: Proceedings of the Workshop on Wikipedia and Artificial Intelligence: An Evolving Synergy, pp. 7–12. AAAI Press, Menlo Park (2008)Itakura, K.Y., Clarke, C.L.: Using Dynamic Markov Compression to Detect Vandalism in the Wikipedia. In: SIGIR 2009: Proc. of the 32nd Intl. ACM Conference on Research and Development in Information Retrieval, pp. 822–823 (2009)Chin, S.C., Street, W.N., Srinivasan, P., Eichmann, D.: Detecting Wikipedia Vandalism with Active Learning and Statistical Language Models. In: WICOW 2010: Proc. of the 4th Workshop on Information Credibility on the Web (April 2010)Zeng, H., Alhoussaini, M., Ding, L., Fikes, R., McGuinness, D.: Computing Trust from Revision History. In: Intl. Conf. on Privacy, Security and Trust (2006)McGuinness, D., Zeng, H., da Silva, P., Ding, L., Narayanan, D., Bhaowal, M.: Investigation into Trust for Collaborative Information Repositories: A Wikipedia Case Study. In: Proc. of the Workshop on Models of Trust for the Web (2006)Adler, B., de Alfaro, L.: A Content-Driven Reputation System for the Wikipedia. In: WWW 2007: Proceedings of the 16th International World Wide Web Conference. ACM Press, New York (2007)Belani, A.: Vandalism Detection in Wikipedia: a Bag-of-Words Classifier Approach. Computing Research Repository (CoRR) abs/1001.0700 (2010)Potthast, M., Stein, B., Holfeld, T.: Overview of the 1st International Competition on Wikipedia Vandalism Detection. In: Braschler, M., Harman, D. (eds.) Notebook Papers of CLEF 2010 LABs and Workshops, Padua, Italy, September 22-23 (2010)Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)Davis, J., Goadrich, M.: The relationship between Precision-Recall and ROC curves. In: ICML 2006: Proc. of the 23rd Intl. Conf. on Machine Learning (2006

A Spinning Wheel for YARN: User Interface for a Crowdsourced Thesaurus

YARN (Yet Another RussNet) project started in 2013 aims at creating a large open thesaurus for Russian using crowdsourcing. This paper describes synset assembly interface developed within the project — motivation behind it, design, usage scenarios, implementation details, and first experimental results