Wikipedia survey I (Respondent profiles)

This is the first in a series of posts about the results of my survey of Wikipedians who have translated content for the Wikimedia projects (e.g. Wikipedia). Because I’ve already submitted an article analyzing the survey, these posts will be less analytical and more descriptive, although I will be able to discuss some of the survey questions I didn’t have space for in the paper. This post will look at the profiles of the 76 Wikipedians who responded to the survey (and whom I’d like to thank once again for their time).

Survey Methodology
I wanted to randomly invite Wikipedia translators to complete the survey, so I first consulted various lists of English translators (e.g. the Translators Available page and the Translation/French/Translators page) and added these usernames to a master list. Then, for each of the 279 languages versions on the List of Wikipedias page*, I searched for a Category: Translators page for translations from that language into English (ie. Category: Translators DE-EN, Category: Translators FR-EN, etc.). I added the usernames in the Category: Translators pages to the names on the master list, and removed duplicate users. This process led to a master list with the names of 1866 users who had volunteered to translate Wikipedia content into English. I then sent out invitations to 204 randomly selected users from the master list, and 76 (or 37%) of them responded. A few caveats: additional Wikipedians have probably translated content for the encyclopedia without listing themselves on any of the pages I just mentioned. Moreover, anyone can generally edit (and translate) Wikipedia pages without creating an account, so the results of the survey probably can’t be generalized for all English Wikipedia translators, let alone Wikipedia translators into the other 280 languages, who are not necessarily listed on the English Wikipedia pages I consulted. Finally, although 76 Wikipedians may not seem like many respondents, it is important to note that many of the users on the master list did not seem to have ever translated anything for Wikipedia: when I consulted their user contribution histories, I found that some Wikipedians had added userboxes to their profile pages to indicate their desire to translate but had not actually done anything else. I was interested only in the views of people who had actually translated, so the 76 respondents actually represents a much larger share of actual Wikipedia translators than it appears.

Profiles
The vast majority of the respondents (64 respondents, or 84%) were male and most were 35 years of age or younger (57 of the respondents, or 75% were under 36). This result is not surprising, given the findings of a 2008 general survey of more than 176,000 Wikipedia users, where 50% of the respondents were 21 years of age or under (in all, 76% were under 30) and 75% were male.

When respondents were asked about translation-related training, most (51 respondents or 68%) responded that they had no formal training in translation. Here’s a graph with a breakdown for each response:
Wikipdia translators-training

Given that respondents were generally young and usually did not have formal training in translation, it’s not surprising that 52 of the 76 respondents (68.4%) had never worked as translators (ie. they had never been paid to produce translations). Only 11 respondents (or about 14%) were currently working as translators on a full- or part-time basis, while 13 (or about 17%) had worked as translators in the past but were not doing so now. So it’s not surprising either that only two respondents were members of a professional association of translators.

Finally, when asked about their current occupations, respondents reported working in range of fields. I’ve grouped them as best I could, using the Occupational Structure proposed by Human Resources and Development Canada. Two respondents did not answer this question, but here’s an overview of the 74 other responses:

Occupation No. of respondents Percentage
Student
    6 High school students
    4 College/University students (languages)
    17 College/University students (other fields)
27 36%
Works in IT sector 11 15%
Works in language industry 9 12%
Works in another sector (e.g. graphic design, law, education) 8 11%
Works in business, finance or administration 7 9%
Unemployed/stay-at-home parent/retired 5 7%
Academic 3 4%
Engineer 2 3%
Works in sales and service industry 2 3%
Total: 74 100%

Later this week (or early next week), I’ll look at the types of crowdsourced translation initiatives the respondents were involved in (other than Wikipedia, of course), and the roles they played in these initiatives. After that, I’ll discuss respondent motivations for volunteering and the impact their participation has had on their lives.


* There are now 281 Wikipedia versions.

Survey on crowdsourced translation initiatives launched

This weekend, I finally began sending out the invitations for the survey I’ve been preparing on crowdsourced translation initiatives. It asks respondents about their backgrounds, whether they have any formal training in translation, why they have decided to participate (or not to participate) in crowdsourced translation projects, and whether their participation has impacted their lives (e.g. whether they received job offers or met new colleagues because of their participation).

I’ve begun with Wikipedia, but I plan to invite respondents who have participated in other crowdsourced translation initiatives, including TedTalks, Kiva and Global Voices Online. I’ve just finished randomly sampling the Wikipedians who have helped translate content into English, but I will now start randomly sampling those who have translated from English into French, Spanish and/or Portuguese. I’m hoping to determine whether participant profiles differ from one project to another: for instance, does the average age of participants vary from one project to another? Do some projects seem to attract more people who have some formal training in translation? Do motivations differ when participants are translating for non-profit initiatives vs. for-profit companies?

Responses have started to trickle in, and I’m already starting to see some trends, but I won’t say anything more until all of the surveys have been submitted and I’ve had a chance to analyze the results. If you’re interested in finding out more details about the survey, please let me know. And if you want to see some of the results, check back in a few months. I expect to have some details to discuss by late March or early April.

Jeff Howe on Crowdsourcing

As I mentioned in my last post, I’m in the midst of writing two articles on crowdsourcing and translation, which means I’m busy reading some background material on the topic. I thought I’d post a few quick reviews of the books I’m reading, in case someone else is interested in finding out more about how crowdsourcing can change (and in some cases has changed) the translation process.

Jeff Howe’s Crowdsourcing: Why the Power of the Crowd is Driving the Future of Business is a good introduction to the crowdsourcing phenomenon. According to Howe, crowdsourcing emerged due to four factors: 1) a rising amateur class, 2) the development of open-source software that inspired these amateurs and provided them with a platform to contribute to tasks, 3) the proliferation of the Internet (and cheaper tools for such tasks as photography, film making and graphic design), and 4) the evolution of online communities, which helped organize people into “economically productive units” (2008: 99).

Howe offers a plethora of examples of crowdsourcing in action, with detailed profiles of such ventures as Threadless.com, where people design, vote on, and then purchase winning T-shirt designs, iStockphoto, a community of amateur photographers selling their photos for a nominal fee, and InnoCentive, a network of scientists that help solve R&D problems for fortune 500 companies such as Procter & Gamble (and are paid a financial reward for doing so).

With these kinds of examples, Howe illustrates how crowdsourcing is changing the way work is done. He uses the collaborative effort of Linux, for instance, to show how software can be developed more quickly than with traditional, “heavily managed, hierarchical approach” (2008: 55) and still contain very few bugs. With the InnoCentive example, he shows how problems can be solved by a fresh set of eyes from outside the field and how crowdsourcing often results in a meritocracy, where people are judged on the product they produce rather than their nationality or professional qualifications (2008: 45-46).

I didn’t find any examples of crowdsourced translation initiatives, but Howe does raise some interesting questions about how crowdsourcing challenges traditional concepts, such as how we define the term “professional.” He argues that because information is so readily available on the Internet, “amateurs are able to use the Web to acquire as much information as the professionals” (2008: 40). And that poses problems when we try to determine what makes someone an “amateur” and someone else a “professional”:

relying on financial information to draw distinctions between professional and nonprofessional is a good rule of thumb if you prepare tax returns for a living. But if you’re looking at crowdsourcing, it only produces confusion. What is evident in crowdsourcing is that people with highly diverse skills and professional backgrounds are drawn to participate. While very few iStock contributors are professional photographers, more than half have had at least one year of formal schooling in “art, design, photography, or related creative disciplines” (2008: 27-28).

I do, however, have two complaints about the book. First, the author often doesn’t fully cite his sources, making it hard for readers to fact check or get more information about something Howe says. For instance, on page 15, I came across this tantalizing reference:

A study conducted by MIT examined why highly skilled programmers would donate their time to open source software projects. The results revealed that the programmers were driven to contribute for a complex web of motivations, including a desire to create something from which the larger community would benefit as well as the sheer joy of practicing a craft at which they excel.

Now, since I’m trying to determine why people volunteer to translate websites and other texts, I would really like to take a look at this MIT study to find out about this “complex web of motivations” and to see how the survey was designed. Unfortunately, Howe doesn’t provide the date, authors or title of the publication where he found this information, so I’m out of luck. I realize that Howe’s book is published by a trade publisher rather than an academic press, but it does include endnotes with bibliographic details for a number of other references, so there’s no reason for this reference to be missing. (Incidentally, I did manage to find several papers on the motivations of open-source developers, and I’ve listed them at the end of this post, in case anyone is interested. One even appears in a volume published by MIT.)

My second complaint is that Howe seems to have assumed that few people will read through his book from beginning to end (as I did). Otherwise, why would he repeat sentences (and sometimes paragraphs) in multiple chapters. For instance, I found these three sentences on pages 134 and 159, when Howe describes “idea jams”, or the use of crowdsourcing to generate new ideas:

People have pointed out that this is little more than an Internet-enabled suggestion box. Just so. The Internet didn’t make crowdsourcing possible–it just made it vastly more effective.

Despite my two quibbles, though, this is an interesting and very accessible book that explores various facets of crowdsourcing (from for-profit initiatives like YouTube and MySpace, which make money selling advertising around user-generated content, to projects like Wikipedia and the futures market like the Iowa Electronic Markets). If you’re at all intrigued by the phenomenon, it’s worth a read.

References:
Freeman, Stephanie. (2007). The Material and Social Dynamics of Motivation: Contributions to Open Source Language Technology Development. Science Studies, 20(2): 55-77 [available online here].

Ghosh, Rishab Aiyer. (2005) Understanding Free Software Developers: Findings from the FLOSS Study. In Joseph Feller, Brian Fitzgerald, Scott A. Hissam & Karim R. Lakhani (eds). Perspectives on Free and Open Source Software. Cambridge: MIT Press.

Hars, Alexander & Shaosong Ou. (2002). Working for Free? Motivations for Participating in Open-Source Projects. International Journal of Electronic Commerce, 6(3): 25-39.

Howe, Jeff. (2008). Crowdsourcing: Why the Power of the Crowd is Driving the Future of Business. New York: Crown Business.

The ethics of crowdsourcing

I’m almost finished my paper on translation blogs, and I’m getting ready to move on to my crowdsourcing projects. That’s why I was glad to hear that the editors of Linguistica Antverpiensia accepted my proposal for a special issue on community translation. Here’s what I plan to write about:

If, as Howe (2008: 8 ) argues, “labour can often be organized more efficiently in the context of community than it can in the context of a corporation[,] the best person to do a job is the one who most wants to do that job[,] and the best people to evaluate their performance are their friends and peers who […] will enthusiastically pitch in to improve the final product, simply for the sheer pleasure of helping one another and creating something beautiful from which they will benefit,” crowdsourcing raises some ethical questions. What, for instance, are some of the implications of for-profit companies benefiting financially from user communities who help create something from which not only the users will benefit but also the companies themselves? What effects might a user’s interest in project or commitment to a cause have on his or her translation? If crowdsourcing makes available translations that would otherwise not be produced or which would be available only after a long delay (e.g. translations into “minor” target languages, translations of less relevant texts, such as discussion forums), is this reward enough for the community, or do members deserve other forms of remuneration as well? What effects might these forms of remuneration have on community members, professional translators, non-profit and for-profit organizations, and users outside the community? Using examples of crowdsourced translation initiatives at non-profit and for-profit organizations, including Kiva, Global Voices Online, Asia Online, Plaxo and TEDTalks, this paper will explore various ethical questions that apply to translation performed by people who are not necessarily trained as translators or remunerated for their work. To better explore questions related to translation into major and minor languages, this paper will contrast the target languages offered through these crowdsourced initiatives with those offered via the professionally localized websites of five top global brands. It will also search for answers to these ethical questions by comparing the principles shared by the codes of ethics of professional translation associations in fifteen countries.

As I’ll be working on this paper between now and April 2011, I’d be very interested to hear from anyone who has worked on a community translation project, as a translator, an editor, developer, organizer, etc. What are your thoughts on the ethics of crowdsourcing? Leave me a comment or contact me over the next few months and let me know your point of view.

January 2012 Update: My article on the ethics of crowdsourcing is now available. It was published in Linguistica Antverpiensia 10 (2011), the theme of which was “Translation as a Social Activity–Community Translation 2.0.” The table of contents is available here.

Is “cognitive surplus” behind social translation?

This morning, I was catching up on the BBC’s Digital Planet podcasts while I was out for a jog, and I heard this interview with Clay Shirky, who argues that worldwide, one trillion hours of spare human time is available on a yearly basis for collaborative efforts such as Wikipedia. He refers to these hours as cognitive surplus, and he asserts that this surplus exists not because the tools for online collaboration have recently become available, but rather because people are “motivated to behave in the ways they’re now given the opportunity to behave in.” Thus, public collaborative efforts like Wikipedia exist not because we have wikis, but rather because people care about the project and are now able to make use of Internet tools to help them create an encyclopedia that will benefit both the community and society in general. As the BBC interviewer remarks, Shirky is optimistic about the potential for cognitive surplus: he believes it is being and will be used for the greater good rather than for malicious purposes, such as marginalizing a particular community.

Shirky has also given a TED Talk on his concept of cognitive surplus, and you can find it on YouTube here. In this presentation, he uses the crisis mapping platform Ushahidi as an example of an open-source collaborative effort that spread from a single user in East Africa to global use in just three years. He describes cognitive surplus as the ability of the world’s population to volunteer and cooperate on large, often global projects. It is composed of the world’s free time and talent (the 1-trillion-plus hours mentioned in the BBC podcast) and the online tools that allow the world to actively create, share and consume products rather than just passively consume products such as television programs. He then contrasts the two types of projects that can be developed through cognitive surplus: those with communal value (such as LOLCats, whose value is created by the participants for one another) and those with civic value (such as Ushahidi, whose value is created by the participants but enjoyed by society as a whole). The goals of a project with civic value is to make life better not just for the participants but for everyone in the societies in which the project is operating. As Shirky argues, when organizations are arranged around a culture of generosity, they can achieve significant results without contractual overhead.

Shirky is essentially speaking about crowdsourcing, although he doesn’t mention the term. And although he doesn’t bring up translation as an example of how cognitive surplus can be used, social translation, which I have discussed here and here, could be (and is) one of the tasks on which cognitive surplus is spent. Thus, texts could be translated to benefit a community (e.g. fansubbing of anime) or to benefit society in general (e.g. blog postings at Global Voices Online).

But some of Shirky’s arguments raise some ethical questions, particularly with respect to collaborative translation projects. First, I think Shirky’s proposal to classify collaborative efforts as having either communal or civic value is problematic. Can all collaboration really be considered one or the other? Do projects change over time? Who decides whether a project has communal or civic value—the community or those outside it? For instance, fansubbing would likely be considered to have communal value, since the subtitling is not intended to benefit society as a whole but rather the community of anime fans who are unable to understand Japanese. And yet, subtitles make these videos accessible to anyone who wants to watch anime in a language other than the original, whether or not the viewer considers him- or herself to be part of the community of anime fans. This is also the case for the subtitled TED Talks, which make high-level presentations available to viewers around the world, whether they are part of the community who watches them on the TED website, or whether they are outside the community and simply come across one of the subtitled videos on YouTube (like this one, for instance). And what about a project like Facebook, which has a community of 500 million users around the world: when the cognitive surplus of people around the world is used to make Facebook available in languages other than English, this is benefiting the Facebook community, isn’t it? But won’t it also benefit society as a whole, since people who were not previously on Facebook due to their inability to understand English may now enjoy accessing this free service in French, Spanish, Arabic, Italian, German, etc. And it certainly benefits Facebook, but Shirky does not have a category for that. Translation, by its nature, makes information available to communities other than the original target audience. When cognitive surplus is used to produce translation, projects with communal value become projects with civic value.

And finally, I wonder whether efforts with civic value always as beneficial as Shirky posits. Collaborative translation does, of course, have many positive effects (see my last post on this topic or this recent Translorial article). Yet for-profit organizations that rely on cognitive surplus to get their material translated are sliding down a steep ethical slope. Who benefits more from Facebook’s translated platforms, the community, society in general or Facebook? How is the public perception’s of translation as a professional activity affected by calls for amateurs to collaborate on translation projects? And, if crowdsourcing makes available translations that would otherwise not be produced or which would be available only after a long delay (e.g. translations into “minor” target languages), is this reward enough for the community, or do members deserve other forms of remuneration as well? These are questions that need to be addressed before we can really decide whether harnessing cognitive surplus for projects with civic value will indeed change society for the better, as Shirky contends.

Crowdsourcing: One of the top two threats to professional translators?

According to a recent recent article in Translorial, the journal of the Northern California Translators Association, the American Translators Association Board had just declared crowdsourcing one of the top two threats to the profession and the association. It was tied with the economic downturn.

A companion piece that was also part of the February 2010 issue of Translorial offers a brief summary—and a link to the video recording—of a talk from the 2009 general meeting of the Northern California Translators Association. The talk was entitled “New Trends in Crowdsourcing: The Kiva/Idem Case Study,” and it was given by Monica Moreno, localization manager at Idem Translations, and Naomi Baer, Director of Micro-loan Review and Translation at a not-for-profit microfinancing organization called Kiva. (Baer, incidentally, is also the author of the first Translorial article I cited).

Despite the ATA’s rather dour opinion of crowdsourcing, both the Translorial article and the presentation by Moreno and Baer offer a fairly positive view of the opportunities crowdsourcing provides not just to the companies that turn to volunteers for their translation needs, but also to web users, minority-language communities, and even professional translators. After all, as Moreno and Baer noted, languages that are considered Tier 2 or lower by corporations are often used in crowdsourcing initiatives. Just look at the TED Open Translation Project , one of the crowdsourcing initiatives cited in the presentation.

As of March 26, 2010, TEDTalks have been subtitled into more than 70 languages, including Swahili, Tagalog, Tamil, Icelandic and Hungarian. More than 400 talks have been subtitled in Bulgarian, nearly 300 in Arabic, and more than 200 in Romanian, Polish and Turkish. And these figures compare favourably with traditional Tier 1 languages: French (304 talks), Italian (263 talks), German (195 talks) and Spanish (575 talks). By comparison, large localization projects by commercial organizations don’t usually offer as many languages: Of Google, Microsoft and Coca-Cola, which topped the 2009 Global Brands ranking published in the Financial Times, Coca-Cola appears to have been localized for the most country and language pairs, with a whopping 124 countries and 141 locales, while Microsoft is a close second at 124 locales. However, many of the links to Coca-Cola sites (e.g. nearly all of the 44 African locales) actually take users to the US English site, so Coke probably offers closer to just under 100 locales, many of which (e.g. 13 of the 30 Eurasia locales) are actually English-language versions. Likewise, IBM, the fourth-ranked brand, offers 100 locales, but 49 of them are English-language versions, and another 10 are in Spanish. So, while some of the largest brands initially appear to have targeted more linguistic groups, the TEDTalks have actually been made available in more languages.

In addition, smaller linguistic communities within a region are not often targeted by the larger corporations, as these groups may not have the purchasing power to justify translation costs. Microsoft, Coca-Cola, IBM, Procter & Gamble, and Ikea, for instance, all offer their Spain websites only in Spanish, while some TED videos (as well as Google) are available in Catalan and Galician. With non-profit initiatives, where users may feel driven to contribute their time to support a particular cause or to make available information (like the TED talks) that would otherwise be inaccessible to those who don’t speak the source language, crowdsourcing can help reduce the language hierarchy that for-profit localization initiatives encourage: the translations are user-generated and sometimes user-initiated, so as long as enough members of a community feel committed to making information available, they will provide translations into so-called major and minor languages without worrying about a return on investment. What we need now, then, is more research into the quality of the translations produced by volunteer, crowdsourced efforts. Making information available in more languages is laudable, but if the translations are inaccurate, contain omissions or have added information, then the crowdsourcing model may not be as advantageous as it appears.

The presentation by Moreno and Baer also offered a few insights into the motivations of volunteer translators: some wanted to give back to the community, others wanted to mentor student or amateur translators without having to make a significant time commitment, while others saw it as a networking opportunity. As Baer noted, her volunteer efforts for Kiva eventually landed her a paid job with the organization. These anecdotal details about translator motivations underscored (at least for me) the need to systematically research the motivations of the people involved in crowdsourced translation projects. I think it’s worth comparing the motivations of those involved in non-for-profit initiatives like TED, Kiva, or Global Voices (which I’ve discussed in a previous post) and those involved in initiatives launched by for-profit companies such as Facebook. I suspect that motivations would differ, but a survey of the volunteers could confirm or refute this hypothesis.

Overall, the presentation by Moreno and Baer is definitely worth watching if you’re at all interested in crowdsourcing and translation. It’s available on Vimo at this address: http://vimeo.com/8549171.

Participatory web and social translation

In a recent article in Slate Magazine, Chris Wilson writes about “the myth of Web 2.0 democracy”, citing a number of research projects that have studied user-generated collaborative knowledge systems such as Wikipedia, Del.icio.us and Digg. As Wilson argues, these sites, which seem on the surface to be excellent examples of participatory democracies, where users collectively contribute to and maintain the content, are actually oligarchies run by a small number of users. Between 2003 and 2004 on the Wikipedia site, for instance, 50% of the edits were made by administrators, who make up a small percentage of Wikipedia users. Some graphs analyzing Wikipedia trends can be found here.

This finding doesn’t surprise me, as my own research revealed that translation networks function in much the same way: only seven percent of TranslatorsCafe members had ever posted a message in the discussion forum between January 2003, when the site was founded, and March 2007, when I wrote an article for Meta about how interactions occur in translation networks. Likewise, just under five percent of members had ever posted a question, answer or comment to the terminology forum between April 2006, when the forum was introduced, and February 2007.

What I did find more interesting, however, were the results of a conference presentation from the 2007 Computer Human Interaction conference in San Jose, which studied whether Wikipedia is maintained by an elite group of users or by “the wisdom of the crowds,” that is, whether a small group of people is creating and maintaining most of the entries, or whether a larger number of people are making a small number of edits to many entries.

The researchers found that while the elite group of users was initially responsible for the highest number of edits, this trend has since shifted:

In the beginning, elite users contributed the majority of the work in Wikipedia. However, beginning in 2004 there was a dramatic shift in the distribution of work to the common users, with a corresponding decline in the influence of the elite (Chi et al 2007: 8).

They found a similar trend on the del.icio.us website, leading them to conclude that the shift in work distribution from the elite to novice users may be a typical phenomenon for online collaborative knowledge systems. They explained this trend in the following way:

For such systems to spread, early participants must generate sufficient utility in the system for the larger masses to find value in low cost participation. Like the first pioneers or the founders of a startup company, the elite few who drove the early growth of Wikipedia generated enough utility for it to take off as a more commons-oriented production model; without them, it is unlikely that Wikipedia would have succeeded. Just as the first pioneers built infrastructure which diminished future migration costs, the early elite users of Wikipedia built up enough content, procedures, and guidelines to make Wikipedia into a useful tool that promoted and rewarded participation by new users (Chi et al. 2007: 8).

What might this mean for collaborative translation projects like those I’ve been discussing for the past few months? First, it points to the need to study exactly how participation in social translation projects change over time. Global Voices, for instance, published a survey of its volunteer translators in October 2009, noting that of the 108 people who had provided a translation in September and responded to the survey, a little under half had started working on translations for the site in 2009, while 38 others had been volunteering since 2008 and another 15 had been involved since 2007. Further research into how participation rates have changed over time would help show whether participants in social translation projects are actively involved for long periods of time, whether an elite group remains involved for a short period and is then replaced by novice users, etc.

Second, these results indicate that in large social translation projects, only a small number of volunteers may, in fact, be participating at any given time. Do motivations vary among the elite/very active and novice/less active users? Both of these questions also need to be answered.

references:
Chi, Ed, Aniket Kittur, Bryan A. Pendleton, Bongwon Suh & Todd Mytkowicz. (2007). Power of the Few vs. Wisdom of the Crowd: Wikipedia and the Rise of the Bourgeoisie. alt.chi 2007. [Online: http://www.viktoria.se/altchi/submissions/submission_edchi_1.pdf].

McDonough, Julie. (2007). How Do Language Professionals Organize Themselves? An Overview of Translation Networks. Meta, 52(4), 793-815. [abstract] [Full text (html)] [Full text (PDF)].

Social Translation II

While looking for an article I had read in the Journal of Specialized Translation a few weeks ago, I came across another one by accident, and as it turns out, it discusses social translation (although the author refers to the concept as user-generated translation). Saverio Perrino’s User-generated Translation: The future of translation in a Web 2.0 environment appears in Issue 12 of JOST.

In it, Perrino reviews seven tools for social/collaborative/user-generated translation: ProZ, TranslatorsCafe, Traduwiki, Der Mundo, Cucumis, Wiki Translate and Word Reference. His review of ProZ and TranslatorsCafe focuses only on the user-generated dictionaries and glossaries (KudoZ and TCTerms, respectively) within these sites, as these aspects can directly be considered user-generated translation.

In his review of Der Mundo (formerly known as Worldwide Lexicon), Perrino makes an important point about the motivations behind social translation. He notes that the site founder and designer, Brian McConnell, “underestimated the fact that volunteer translators would not work on any text, but only on those they were genuinely interested in.” McConnell eventually redesigned Worldwide Lexicon so that volunteers could choose the texts they wanted to translate instead of being assigned portions of a text in which they might have very little interest. This point was also raised by Zuckerman in the CBC interview I quoted in my last post on this topic. He noted that the crowd-sourced translations are usually of “extremely high quality,” in part because they’re “done by people who really care about the content that they’re doing.” Later, he added that “particularly if it’s on something that you care about, the content is interesting to you, particularly if you have a peer group to rely on, particularly if you have the choice to engage in this process, it turns out that people will flock towards projects like this.”

Clearly, any research into the motivations of those engaged in social translation projects should explore the extent to which a volunteer’s interest in a topic or his or her commitment to a cause affects not only the decision to participate in the project, but also the extent of participation. For instance, do users who describe themselves as very interested in a project translate more text than those who are less interested, and do those who feel more committed to a cause take on more roles (e.g. translator, reviser, proof-reader) in a social translation project ?

Social translation

On my last jog, I listened to a podcast from CBC Radio. In it, Nora Young, host of Spark, interviewed Ethan Zuckerman, who runs Global Voices Online, a community of bloggers working to make blogs from around the world available in various languages. The focus of this interview was on what Zuckerman referred to as “social translation”, or crowdsourced translations. He described how Global Voices Online had been translated into twenty languages by bilingual volunteers who strongly felt that the content of various blogs was so important that it should be available to a wider audience. The podcast is available on this webpage.

I found this interview very insightful, as I’ve grown very interested in crowdsourced translations over the past year. Brian Harris has some very interesting posts with examples of this translation practice: Here’s one about crowdsourcing of Haitian text messages, another about Plurk, a Canadian rival for Twitter, one about crowdsourced translations of The Economist magazine in China (an example that was also mentioned by Zuckerman on the CBC podcast), and a final one describing Traduwiki, a site where large texts are broken up into short segments for anyone to translate. A commenter on Brian’s blog also left a link to Translated By You, a website similar to Traduwiki.

I’ve become so interested in social/collaborative/crowdsourced translation because I think there’s significant potential for academic research in this area. First, because the people translating for these projects are often not professional translators but bilinguals who want to help disseminate content, there’s a good opportunity to see whether peer-reviewed volunteer translations by people with varying translation skills and training are as acceptable as translations produced by trained professionals. Second, because the volunteer aspect of social translation leaves room for analyzing the motivations behind those who participate in the project, and translator motivations are one of my research areas. Some websites have established a non-tangible reward system, where translators are recognized for their work by getting a special mention on their profile page, but other websites offer no rewards, and volunteers are presumably translating the content because they want to make it available. Invariably, the people responsible for these projects are very positive about social translation and enthusiastic about how the phenomenon can help make content available for free. Which leads me to the last aspect of crowdsourcing that could be researched: the attitude of professional translators toward collaborative translation initiatives. Various translation blogs have discussed the issue, including The Masked Translator and Musings from an Overworked Translator. The posts have been particularly critical of for-profit companies relying on crowdsourcing to translate their websites for free, but many are open to the idea of providing volunteer translations to non-for-profit organizations. I’m going to spend some time this summer organizing my thoughts on this issue, and then I’ll start preparing a questionnaire to send out to participants of collaborative translation projects to find out why they wanted to participate. That should give me a good idea of where to go next.

Anyone with links to crowdsourcing translation projects not listed here is welcome to email me or add a comment. I’d love to hear from other researchers working in this area.