Wikipedia in higher education; gender-driven talk page conflicts; disease forecasting

Recent research

Wikipedia in higher education; gender-driven talk page conflicts; disease forecasting

Use of Wikipedia in higher education influenced by peer opinions and perception of Wikipedia's quality

A paper titled "Factors that influence the teaching use of Wikipedia in Higher Education"^[1] uses the technology acceptance model to shed light on faculty's (of Universitat Oberta de Catalunya) views of Wikipedia as a teaching tool. The main factors are shown to be the perception of colleagues’ opinion about Wikipedia and the perceived quality of the information on Wikipedia. As the authors note, while prior studies also pointed to the quality concerns, this study suggests a causal link between colleagues' views and one's perception of Wikipedia quality. The authors conclude that the strong peer culture within academia makes the importance of role models very significant, which in turn has implications for the segment of the Wikimedia movement that desires greater ties with the academic world. The authors also note that "despite the lack of institutional support and acknowledgement, a growing number of academics think it is very useful and desirable to publish research results or even intermediate data in open repositories", an attitude that also correlates positively with positive views of Wikipedia. To quote the authors' very valid recommendation: "For those faculty members already using Wikipedia as a learning tool, we think it would have greater impact if they publicly acknowledged their practices more, especially to their close colleagues, and explain their own teaching experiences as well as the effects it has had on the students’ academic performance." The team behind the paper is also partnering in the Wikidata for research project featured in News and notes.

Analysis of two gender-driven talk page conflicts on the German-language Wikipedia

Reviewed by Maximilianklein (talk)

"Gender differences within the German-language Wikipedia"^[2] is a pair of close readings of two gender-driven talk page conflicts on the German Wikipedia from 2006 and 2013, "show[ing] exemplarily that a) the feministic gender discourse in Wikipedia is not appreciated – primarily by male Wikipedians – [...] and b) that discussions behind the scenes of Wikipedia can feature an unpleasant and rude nature, that is not very appealing and motivating for female contributors". The analysis aims to focus on the communication styles of the gendered personalities as viewed under the critical rubrics of Margarete Jäger and Nina Schuppener. In the degenerating arguments around whether or not the welcome message on the German Wikipedia's main page (2006 thread) and German Wikipedia articles in general (2013/14 straw poll talk page) should use generic male pronouns and nouns, or newer more neutral alternatives, like using parentheses in "Mitarbeiter(in)", it is highlighted that the male-appearing participants use instruction and discrediting statements; and the female-appearing tend to question intellectual capabilities and give advice. Finally the authors conclude that "the most crucial point is the fact that the female author gave up [first]," stopping responding less than 24 hours into the discussion, and that the change advocated for was not enacted. These deconstructed examples add to an evidence of a hypothesis that minority voices are crowded out in Open Culture, as purported by the "Free as in Sexist" theory.

Briefly

"Original map by John Snow showing the clusters of cholera cases in the London epidemic of 1854" as seen in the English Wikipedia article Epidemiology.

History of the Spanish Wikipedia's ArbCom: A short recounting by Sefidari and Ortega (pre-print) summarised the history of the Spanish Wikipedia Comité de resolucíon de conflictos (arbitration committee), which existed from 2007 to 2008. It was composed of admins, received complaints which in 80 % of cases involved admins, dismissed nearly all cases presented, ruled against the claimant in a large majority of accepted cases, and was finally dissolved in 2009.^[3]
Two new papers on disease forecasting using Wikipedia: Yet another study (pre-print), considering 5 articles, showed that English Wikipedia page views trends can forecast the peak in influenza-like illnesses in the USA. Essentially, by visiting the articles in question, users are self-reporting their (suspect) disease, some weeks in advance of the data collected centrally by a government agency based on medical practitioners' reports of the same.^[4] Another study, again focused on some English Wikipedia articles, reached the same conclusion with slightly different (and, notably, fully open source) methods, for 14 diseases, while producing a useful list of some dozens past studies on the matter.^[5]
Wikipedia as a source of health information during salmonella outbreak: A statistically significant survey in the Netherlands assessed with what efficacy the population was informed about Salmonella infection during an outbreak in the country. Nearly all information was received passively (mainly from TV, radio and newspapers, but also social media); of the minuscule minority who actively sought information, most turned to their newspaper website, or ended up (with highest satisfaction among all sources) on official websites or Wikipedia.^[6]
Most MoodBar users became longer-term contributors: A study on one dataset produced by the (mostly discontinued) MoodBar tool showed that the newcomers who gave feedback via the MoodBar were significantly more likely to become longer-term contributors. After six months, 3.6% of editors who were able to use the MoodBar were still editing, compared to 3.3% of those who did not have the option.^[7]
New R libraries for Wikipedia research: A new R programming language library "wikipediatrend"^[8] that facilitates longitudinal page-view analyses has been created. The package is a wrapper on top of long-time service stats.grok.se. This marks an uptick in the popularity of the R language for Wikipedia analysis as WikipediR was also recently released which itself wraps many common mediawiki API calls.
Use of Wikinews to teach journalism students: This paper^[9] discusses an educational project that used Wikinews in an undergraduate journalism course at the Australian University of Wollongong. While the use of Wikipedia in education has dominated the relevant discussions, Wikinews seems like a valuable, yet underused tool for journalists-in-training. Though this essay-like paper seems to describe the experience in a positive fashion, it does not contain any specific conclusions, nor a list of articles edited by the students that would allow for a more-in depth commentary in the context of the Wikimedia learning experience.
"Linking Today's Wikipedia and News from the Past": This workshop paper^[10] presents a method to automatically identify articles in the New York Times archive matching a particular event mentioned on Wikipedia (dataset)

Other recent publications

A list of other recent publications that could not be covered in time for this issue – contributions are always welcome for reviewing or summarizing newly published research.

"An Empirical Study of Motivations for Content Contribution and Community Participation in Wikipedia"^[11] From the abstract: "The research findings show that content contribution is more driven by extrinsically oriented motivations, including reciprocity and the need for self-development, while community participation is more driven by intrinsically oriented motivations, including altruism and a sense of belonging to the community."
"Wikipedia as a Time Machine"^[12] (presented at WWW 2014)
"Hacking Trademark Law for Collaborative Communities"^[13] (related website: http://collabmark.org/ )
"The political economy of wilkiality: a South African inquiry into knowledge and power on wikipedia"^[14] (PhD Thesis)
"Predicting Low-Quality Wikipedia Articles Using User's Judgements"^[15] From the abstract: "In this paper, we utilize article ratings from Wikipedia users for the first time to assess article quality. We define 'low-quality' based on those ratings and design automatic methods to identify potential low-quality articles."
"Infoboxer: Using Statistical and Semantic Knowledge to Help Create Wikipedia Infoboxes"^[16]
"On the Use of Reliable-Negatives Selection. Strategies in the PU Learning Approach for Quality Flaws Prediction in Wikipedia."^[17]

References

^ Meseguer Artola, Antoni; Eduard Aibar Puentes; Josep Lladós Masllorens; Julià Minguillón Alfonso; Maura Lerga Felip (2014-12-11). "Factors that influence the teaching use of Wikipedia in Higher Education" (Article). Journal of the Association for Information Science and Technology. 67 (5): 1224–1232. doi:10.1002/asi.23488. hdl:10609/39441. S2CID 13566791.
^ Sichler, Almut; Elizabeth Prommer (2014-12-22). "Gender differences within the German-language Wikipedia". ESSACHESS - Journal for Communication Studies. 7 (2(14)): 77–93. ISSN 1775-352X.
^ Sefidari, Maria; Felipe Ortega (2014-12-10). "Evaluating arbitration and conflict resolution mechanisms in the Spanish Wikipedia". arXiv:1412.3695 [cs.CY].
^ Hickmann, Kyle S.; Geoffrey Fairchild; Reid Priedhorsky; Nicholas Generous; James M. Hyman; Alina Deshpande; Sara Y. Del Valle (2014-10-22). "Forecasting the 2013–2014 Influenza Season Using Wikipedia". PLOS Computational Biology. 11 (5): e1004239. arXiv:1410.7716. doi:10.1371/journal.pcbi.1004239. PMC 4431683. PMID 25974758.
^ Generous, Nicholas; Geoffrey Fairchild; Alina Deshpande; Sara Y. Del Valle; Reid Priedhorsky (2014-11-13). "Global Disease Monitoring and Forecasting with Wikipedia". PLOS Comput Biol. 10 (11): e1003892. arXiv:1405.3612. Bibcode:2014PLSCB..10E3892G. doi:10.1371/journal.pcbi.1003892. PMID 25392913.
^ Velsen, Lex van; DesiréJMA Beaujean; Julia EWC van Gemert-Pijnen; Jim E. van Steenbergen; Aura Timen (2014-01-31). "Public knowledge and preventive behavior during a large-scale Salmonella outbreak: results from an online survey in the Netherlands". BMC Public Health. 14 (1): 100. doi:10.1186/1471-2458-14-100. ISSN 1471-2458. PMC 3913330. PMID 24479614.
^ Ciampaglia, Giovanni Luca; Dario Taraborelli (2014-09-04). "MoodBar: Increasing New User Retention in Wikipedia through Lightweight Socialization". Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. pp. 734–742. arXiv:1409.1496. doi:10.1145/2675133.2675181. ISBN 9781450329224. S2CID 2285423.
^ Meissner, Peter. "Introduction to Public Attention Analytics with Wikipediatrend". Retrieved 31 December 2014.
^ Blackall, David (2014). "Learning skills in journalistic skepticism while recognising whistleblowers" (PDF). The European Conference on Education 2014 Brighton, United Kingdom Official Conference Proceedings. Naka Ward, Nagoya, Aichi Japan: The International Academic Forum (IAFOR). ISSN 2188-1162.
^ Mishra, Arunav (2014). Linking Today's Wikipedia and News from the Past. Proceedings of the 7th Workshop on Ph.D Students. PIKM '14. New York, NY, USA: ACM. pp. 1–8. doi:10.1145/2663714.2668048. ISBN 978-1-4503-1481-7. / preprint PDF
^ Xu, Bo; Dahui Li (2015). "An Empirical Study of Motivations for Content Contribution and Community Participation in Wikipedia". Information & Management. 52 (3): 275–286. doi:10.1016/j.im.2014.12.003. ISSN 0378-7206. S2CID 13156558.
^ Stewart Whiting, Joemon M. Jose, Omar Alonso: Wikipedia as a Time Machine. WWW’14 Companion, April 7–11, 2014, Seoul, Korea. PD
^ Welinder, Yana; Stephen LaPorte (2014-08-05). Hacking Trademark Law for Collaborative Communities. Rochester, NY: Social Science Research Network. SSRN 2476779.
^ Ovesen, Håvard (2014). "The political economy of wilkiality: a South African inquiry into knowledge and power on wikipedia". {{cite journal}}: Cite journal requires |journal= (help)
^ Zhang, Ning; Lingyun Ruan; Luo Si (2015-01-01). "Predicting Low-Quality Wikipedia Articles Using User's Judgements". In Elisa Bertino; Sorin Adam Matei (eds.). Roles, Trust, and Reputation in Social Media Knowledge Markets. Computational Social Sciences. Springer International Publishing. pp. 91–99. doi:10.1007/978-3-319-05467-4_6. ISBN 978-3-319-05467-4.
^ Roberto Yus, Varish Mulwad, Tim Finin, and Eduardo Mena: "Infoboxer: Using Statistical and Semantic Knowledge to Help Create Wikipedia Infoboxes" PDF
^ Edgardo Ferretti, Marcelo Errecalde, Maik Anderka, Benno Stein: On the Use of Reliable-Negatives Selection. Strategies in the PU Learning Approach for Quality Flaws Prediction in Wikipedia. In: Proceedings of the 25th International Workshop on Database and Expert Systems Applications (DEXA’14): 11th International Workshop on Text-based Information Retrieval (TIR’14), Munich, Germany, 2014. IEEE. PDF

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31 December 2014 (all comments)

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I found it interesting that in 95% of the cases presented to Spanish ArbCom were dismissed in 2008. Tutelary (talk) 00:05, 3 January 2015 (UTC)[reply]

Sichler and Prommer address an important question, unfortunately they overstate its role saying

However, none of the studies has focussed on the communication style of the collaborative network in order to answer the question why and how this female underrepresentation could be explained and ideally balanced.

Firstly I am not certain that this is a valid reading of the existing research.

However laying that aside, it is important to realise that gender gap can be categorically stated to require more to explain it than "the communication style of the collaborative network". This basically is a variation the naive response of many years ago that "men are rude, therefore women don't edit". We have established to a reasonable degree that what is perceived as a "hostile editing environment" is as discouraging to males as to females. The reasons that we have the gender gap we do are primarily that females do not start editing, and secondarily that when they do edit, they edit less, and for a shorter period. While the communication style is important, given what other research has established, it can only be expected to explain a relatively small part of the secondary problem, and hence changing it will by no means "balance" the gender gap.

Sadly the substantive part of the paper is (as the authors put it) "exemplary" (I would rather say "anecdotal", but maybe they actually meant "exemplary", though in which of its senses would be hard to divine) - and examines a couple of threads for conformance to other authors' characterisations of male and female discourse, with mixed results. It then draws some conclusions out of thin air for example: "She obviously felt offended or did not believe that the conversation would come to an amicable or at least reasonable end."

By the discussion section the paper is falling apart. It starts with a 108 word sentence that even the users of agglutinative languages might recoil at - it has been split into two paragraphs, by some sensitive copy-editor perhaps, in the middle of the phrase "prevents Wikipedia". It continues with the delightful "Gender diversity should be understood as a chance to spread up Wikipedia’s angle..." Most of the rest of the discussion is taken up with more literature review, there are attempts to propose solutions, but these are based upon arguments from someone who's biography got deleted, rather than the results of the analysis presented by the authors.

For reference the conclusion seems to be "Thus, not only women need to be trained to survive the hostile environment, maybe the competitive concept of knowledge production needs to be changed." - the "competitive concept" here seems to be the tactic of out-waiting a disputant.

The final sentence of the paper is so abominably written that it almost defies understanding.

The authors promise - that "Our findings will be analysed with regard to the impact that Wikipedia has as a source of knowledge on its users and producers considering the public discourse" is not redeemed.

How anyone can believe that analysis of two talk-page conversations can lead to robust scientifically based proposals to improve the gender gap, or indeed anything other than a list of research questions is beyond me. Even more so, given the effort that must have gone into producing a 17 page paper, merely in terms of literature review and typing, is why anyone would publish such a paper without asking a native English speaker to at least read through and pick out the solecisms.

All the best: Rich Farmbrough, 01:14, 3 January 2015 (UTC).

Anecdata and data based on small samples is very common within anthropology and ethnography, and is a perfectly valid approach to research. I'm not actually sure what the point of most of your message is, however; it seems to be railing against the injustices of runon sentences in the paper. Unless the researchers read the signpost (or frankly, even if they do), this is just...heh. "instruction and discrediting statements". Ironholds (talk) 15:54, 3 January 2015 (UTC)[reply]

@Ironholds: You may find my few lines more tiring to re-read than I found the original 17 pages, so I will summarize.

The authors do not appear to be familiar with existing research that is relevant to theirs.
They make promises that they don't fulfil
They pluck conclusions out of thin air
They base arguments on random comments from opinion pieces
No one proof read the paper, and the English is appalling, leading to ambiguities of meaning.

One item that may have been of interest (and of use) had it been phrased as a RQ, and applied to a significant amount of data

RQ1 To what extent do talk page contributions of gender identifiable editors on Wikipedia follow existing models of gender differentiation of on-line communication?

Questions of this subtlety are regularly answered by other researchers.

All the best: Rich Farmbrough, 21:58, 3 January 2015 (UTC).

Is reciprocity an extrinsic motivation?

Reciprocity isn't actually an extrinsic motivation: "reciprocity is significantly stronger when extrinsic motivation can be ruled out." EllenCT (talk) 08:58, 3 January 2015 (UTC)[reply]

The extrinsic/intrinsic motivation talked about in that paper is the apparent motivation for the first act. [T]he effect that the motivation of an agent has on the reciprocating behavior of another agent. All the best: Rich Farmbrough, 23:07, 3 January 2015 (UTC).

@Rich Farmbrough: do you agree with the implication in [1] that reciprocity is an extrinsic motivation? I think the senses of justice and fairness are often considered intrinsic because third parties are usually in agreement about such scenarios when they have the same information. EllenCT (talk) 00:26, 4 January 2015 (UTC)[reply]

I'm not sure. It seems to me that reciprocity can be concrete or abstract - examples from The Gift Relationship might be "I received blood and I want to give back" where the blood received is certainly extrinsic, and abstract "I (or my family) might need blood" - there is in neither case any legal obligation, though one might consider a stronger moral obligation in the first case. Whether a moral code is intrinsic or extrinsic is hard to say - certainly bushido is extrinsic. Conversely in American cases recipients of blood have been required to give (or find donors for) an equal or more often greater amount of blood. This is clearly a form of mandated reciprocity, which is extrinsic. I should, I suppose, read the full paper if I can find a copy. All the best: Rich Farmbrough, 00:45, 4 January 2015 (UTC).

Gender split in 2012 Wikipedia Editor Survey?

As Tbayer (WMF) is one of the authors of this piece, and Maximilianklein's report covers gender issues, this seems as good a place as any to ask Tilman once more what the gender split in the 2012 Editor Survey was. The survey's talk page is full of community members asking for this data, yet all inquiries directed at Tilman over the past half year have been ignored. Can we please have this data – just the simple gender split: x% male, y% female, z% other? It is now over 2 years since the survey ran, and this simple piece of data should take less than a minute to report. Thank you. Andreas JN 466 11:57, 3 January 2015 (UTC)[reply]

This is an extremely important data point. If there are methodological issues, I am sure we can address them. All the best: Rich Farmbrough, 22:17, 3 January 2015 (UTC).

I agree that it is an extremely important data point, Rich, all the more since the WMF has not done an editor survey since then. So, if it is ever published, this will be the last data point on the gender gap for the foreseeable future. What do you suggest can be done to get the Foundation to release it? I am at my wits' end: I have asked about it on two mailing lists, I and others have left talk page messages for Tilman in three projects, there are enquiries from John Vandenberg and numerous other editors on the survey talk page in Meta dating back almost two years, yet the only answer I ever got was from Phoebe last month, who said, "I looked through my papers the last time you asked, and I don't think I have it. I'd send it to you if I did." This is an absurd situation: donor money paid for this survey. Yet for years now, the gender split has been shut in some drawer at WMF that nobody wants to open. --Andreas JN 466 12:35, 4 January 2015 (UTC)[reply]

R

Suggested tweaks: point to the CRAN libraries rather than the GitHub ones, and note that WikipediR was released in April 2014. That's not a particularly new thing (or a particularly recent thing - if you've tried writing API client libraries before httr you'll know how much of a pain it was). Ironholds (talk) 15:52, 3 January 2015 (UTC)[reply]

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