Social Rewading in Wiki Systems Motivating the Community Benhad Hoisl 1, Wolfgang Aigne 2, Silvia Miksch 2 1 Institute of Softwae Technology and Inteactive Systems, Vienna Univesity of Technology, Favoitenst. 9-11/188, 1040 Vienna, Austia e0252748@student.tuwien.ac.at 2 Depatment of Infomation and Knowledge Engineeing, Danube Univesity Kems, D.-Kal-Doek-St. 30, 3500 Kems, Austia {wolfgang.aigne, silvia.miksch}@donau-uni.ac.at Abstact. Online communities have something in common: thei success ise and fall with the paticipation ate of active uses. In this pape we focus on social ewading mechanisms that geneate benefits fo uses in ode to achieve a highe contibution ate in a wiki system. In an online community, social ewading is in the majoity of cases based on accentuation of the most active membes. As money cannot be used as a motivating facto othes like status, powe, acceptance, and gloy have to be employed. We explain diffeent social ewading mechanisms which aim to meet these needs of uses. Futhemoe, we implemented a numbe of methods within the MediaWiki system, whee social ewading citeia ae satisfied by geneating a anking of most active membes. Keywods: Social Rewading, Wiki, Online Communities, Motivation, Paticipation, Contibution. 1 Intoduction Wikipedia the most famous fee encyclopaedia has gown to the biggest wiki community site whee hundeds of thousands of uses all aound the wold post and edit aticles in many diffeent languages. The temendous contibution ate on Wikipedia has led to many poblems, like wong infomation, copyight violations, o uses misbehaviou, fo example, spammes o tolls [16]. Othe online communities beside Wikipedia have massive toubles motivating uses to paticipate actively. We ae going to pesent techniques whee the fundamental poblem of both eaching a citical mass of active uses ae addessed. On the one hand, Wikipedia has the poblem that published infomation is not checked fo its accuacy and legality by a fomal pocess of eviewing. Thee has to be a lage and heavily involved community which is coss-checking and poofing infomation fo its coectness voluntaily. Howeve, the opeatos of Wikipedia have not only a social but also a legal esponsibility to publish only coect and faultless
infomation to assue thei ceditability. On the othe hand, many online communities have toubles motivating enough uses to build an active community. Paticipation of membes is the key facto fo a successful online community, and that is why good motivating factos ae essential. As infomation povided ove the Intenet is teated like public goods, poblems like fee iding 1 o social loafing 2 aise. In Wikipedia uses ae not chaged in popotion to thei use, theefoe it appeas ational fo people to view aticles without contibuting anything on thei own. If we assume an economic point of view it can be said that a use has costs by publishing an aticle to Wikipedia (e.g., infomation acquisition and pesentation costs o Intenet connection costs) and theefoe she/he wants something in etun. Extending the benefit fo a use so that it exceeds he/his costs is a good stating point to incease paticipation. With this contibution we ae going to focus on an appoach to motivate uses to paticipate actively in an online community by making use of a numbe of diffeent social ewading techniques [8]. To classify ou appoach, we will give an oveview of elated wok in the next section. Section 3 will explain the developed social ewading techniques while section 4 gives an insight on the calculation pocess of these methods. Section 5 coves the visual appeaance of the authos anking and the implementation is summaized in section 6. A conclusion is dawn in section 7 containing an outlook on futue wok. 2 Related Wok Thee ae numeous books and aticles about the wiki phenomenon (e.g., [5, 11, 14, 18]). Howeve, most wok focuses on technical details, like installing and unning a wiki o the evisioning system and its vantages fo collaboative infomation development. Unfotunately, too little attention is paid to investigate uses behaviou in online communities. Some eseach is done to explain the poblem of fee iding [1, 6] which is likely to occu in times of the Intenet and shaed infomation platfoms. Thee ae also studies about communication activities of uses in vitual communities [17], but the focus is not on motivational factos fo uses of online communities. Which factos ae motivating fo a human being, was aleady discussed by Abaham Maslow and his hieachy of human needs theoy [13]. In an aticle about 1 In this case, fee iding means that a use shouldes less than a fai shae of the costs of the whole infomation poduction of a wiki [3]. If eveybody contibutes the same value of infomation to a wiki, nobody fee ides. One of the biggest poblems is that the value of an infomation esouce to an individual is vey subjective and had to detemine. 2 Social loafing is the phenomenon that pesons make less effot to achieve a goal when they wok in a goup than when they wok alone [9]. As the least aticles in Wikipedia (like in nealy evey othe wiki) ae witten by only one use but in a team the poblem of social loafing is likely to occu. The answe to social loafing ae motivational factos which ae patly solved in the MediaWiki softwae by the possibility to see which sections of an aticle belongs to which autho. So, a contibution is linked to an autho s name and can theefoe be evaluated.
using social psychology to motivate contibutions to online communities [10] an expeiment took place whee the poblems of unde-contibution and social loafing wee addessed. In the aticle, as pedicted by theoy, individuals contibuted when they wee eminded of thei uniqueness and when they wee given specific and challenging goals. As othe pedictions wee disconfimed, esults of the expeiment have to be intepeted caefully. An aticle fom the same co-authos [12] focusing on a elated topic, tied to manipulate two factos to incease paticipation in online communities: similaity how simila goup membes contibutions wee and uniqueness how unique membes contibutions wee within the goup. As a esult both factos positively influenced paticipation. Ou appoach to incease uses paticipation in a wiki is based on accentuation and eputation [15]. By motivating many uses we want to incease the community so that coss-checking takes place and false infomation is automatically soted out. That such an appoach of membe-maintained communities inceases the quantity and quality of contibutions was affimed [4] and empiically tested on Wikipedia [2]. 3 Social Rewading Techniques In this pape we pesent social ewading mechanisms that geneate benefits fo the uses in ode to achieve a highe contibution ate in a wiki community. In ou case, social ewad efes to something that causes a behaviou to incease in intensity. In an online community, social ewading is in the majoity of cases based on accentuation of the most active membes. As money cannot be used as a motivating facto, othes like status, powe, acceptance, and gloy have to be employed. We explain diffeent social ewading methods which aim to meet these needs of uses. The techniques pesented ae focussing pimaily on automatic investigations of quantitative and qualitative chaacteistics of published aticles. As a poof of concept, thee social ewading mechanisms wee implemented using the softwae MediaWiki 3 (which is also used by Wikipedia). Most active membes ae accentuated by applying these social ewading methods to calculate a anking of authos: Amount of Refeences This social ewading method uses Google s SOAP seach API to build an index quality numbe based on thee diffeent citeia: the size of a efeence, the numbe of links pointing to this efeence and the numbe of links pointing to the specific aticle. Rating of Aticles A use centic evaluation of aticles published is still missing in the MediaWiki softwae. We have implemented an open ating system whee uses can vote fo o against an aticle (and optionally leave a comment) by making use of a pedefined pointing scale. Most Viewed Aticles Visits of uses ae counted woking with configued paametes. 3 http://www.mediawiki.og
The two most impotant citeia fo ou choice wee, on the one hand, to find a good mixtue of diffeent methods and, on the othe hand, the level of complexity of the implementation pocess in MediaWiki. We believe that using a couple of diffeent social ewading mechanisms will esult in bette findings fo two easons. The fist eason is because data will be etieved fom diffeent souces. Combining these data should esult in a bette and moe plausible esult than any othe technique alone. The second eason is that many diffeent data souces make it had fo an autho to betay. If we only count uses hits it is obvious that authos would ty to cheat by visiting thei own aticles a lot moe often than othe ones. Of couse thee have to be contol mechanisms, like peventing authos to be counted as visitos of thei own aticles. But too many estictions can falsify the eal behaviou of uses which we ae tying to measue. 3.1 Amount of Refeences As in the case of the Wikipedia encyclopaedia the value of an aticle gows with the amount and quality of used efeences. An appoach to an automated quality check of Intenet esouces was ealized by the help of one of the wold s lagest seach engines: Google. Google can help to detect the quality of an aticle by figuing out how much sites ae linked to a cited efeence 4. If many sites ae linking to an Intenet esouce cited as a efeence and this links themselves have a high numbe on links to them, then infomation displayed on this site must have at least a basic level of plausibility (this concept is the basis fo Google s seach algoithm named PageRank [7]). Besides the numbe of links to a efeence anothe citeion the size of the efeence is used 5. We assume that a efeence with thousands of sub-sites can be moe tusted than a home-made pesonal web-site with only thee pages. A moe global idea is counting the links to a wiki aticle fom outside. By using Google we cannot only check efeences within aticles, but also figue out how many sites outside the wiki ae pointing to an aticle. If thee ae thousands of links to an aticle it is likely that this aticle is valuable to many people. The highe the amount of links to an aticle is, the highe is the fequency of visitos and eades. Many links ae also an indicato fo good quality of an aticle. Ou calculation is influenced by these thee diectly pesented citeia: the numbe of links pointing to a efeence, the size of this efeence and the numbe of links pointing to the specific wiki aticle. Now a quality index numbe of an aticle can be geneated which can be used fo a basic classification of the efeences as moe o less cedibly and which can indicate the publicity of the aticle. So at least an initial quality check of Intenet esouces can be ealized by using Googles PageRank technique. This attempt ties to ank aticles not only by means of quantitative chaacteistics but also qualitative ones. 4 Enteing link:http://www.tuwien.ac.at as a seach tem will esult in showing all pages linking to this addess. 5 By inseting site:http://www.tuwien.ac.at as a seach tem the numbe of sub-sites belonging to this addess will be etuned.
3.2 Rating of Aticles To distinguish good witten aticles fom bad ones, the use has the possibility to vote fo o against it. This is done by asking only one simple question with standadized answe altenatives. Answe possibilities could be: Yes/No ( Did you like the aticle? ); -5 to +5 ( How elevant was the infomation shown in this aticle to you? ) o something simila to that. A text field is inseted giving the use the chance to wite in what she/he liked/disliked. So the ating points ae quantitatively calculated while the autho also gets a pesonal qualitative feedback. The ating esults ae inseted in the discussion page of the aticle. Uses and especially the autho he-/himself can have a quick oveview why uses ated the aticle positively o negatively. As a next step the autho could ewite the aticle based on the ideas of the uses (cetainly uses can do this also on thei own). In the discussion page the autho has the possibility to post answes upon uses comments, thus giving he/him the chance fo a justification. Othe aspects that have to be consideed ae the numbe of minimal votes needed fo a epesentative esult and some sot of potection against multiple votes. 3.3 Most Viewed Aticles The idea behind a list of most viewed aticles is that when an aticle is viewed by many people it is eithe (1) vey infomative and vey well witten with good backgound knowledge of the autho, o (2) it has a highly inteesting theme fo a boad ange of people. If we assume case one then it can be said that aticles which have a high ate of hits o visits help to achieve a good eputation fo thei authos. A list of most viewed aticles can be an oveall list of most viewed aticles eve, sepaated by a cetain amount of time, o they can be categoized by thei topic. A list of most viewed aticles eve can be a good idea, although thee will cetainly not be vey much fluctuation among the top aticles in the list. To avoid this behaviou most viewed aticles of the month o week can be a solution. The following section explains how these diectly pesented social ewading methods ae combined to find out most active uses. 4 Calculation As said befoe ou developed social ewading techniques ae focussing on accentuation of the most active membes in a community. This is done by highlighting the most poductive authos in a anking. In the fome chapte we intoduced the social ewading methods which ae used fo the calculation of such a listing. Now it is time to explain the two-step calculation pocess.
4.1 Revision Basis Each of the thee social ewading mechanisms computes points fo a single evision 6 of evey aticle. This is done by compaing the value of the specific evision with the aveage value of all evisions in the wiki (see equation 1). avg Rj = n i= 1 n R is a set of all evisions whee ij is the value of the j social ewading mechanism of evision i: Ri = { i 1, i 2, i 3, K, ij;1 j 3}. (2) Fo example, fo the social ewading technique Most Viewed Aticles all visits to a evision ae counted. Let us assume aticle A in evision 7 has 20 views. The aveage value of views of all evisions is 30. So evision 7 of aticle A has only 66.67% of the oveall aveage views. As we want to cedit evey evision with a cetain amount of points accoding to thei visits, a scale must be pedefined to set the intevals. In ou scale a value of 66.67% would be gaded with 2 out of 5 points 7. This example of point assignment is done fo evey evision and fo evey social ewading method. Fo mechanism Rating of Aticles uses vote fo an aticle by assigning 0 to 5 points. Fo the technique Amount of Refeences the numbe of links pointing to a efeence, the size of this efeence and the numbe of links pointing to the specific aticle ae used as vaiables. These thee citeia ae weighted accoding to uses settings and ae compaed to a mean value calculated ove all evisions. In the end of the fist computation step fo evey social ewading method and fo evey evision points ae assigned accoding to pedefined scales. These values ae weighted and summed up to an oveall value pe evision. By looking at equation 3 it can be seen that p i ae the summed up points fo evision i fo evey social ewading method j weighted against w j (which has to be defined in the configuation file). p i ij. (1) 3 ij = * w j. (3) avg j= 1 The allocation of points of evisions to authos is done in the next step. Rj 4.2 Autho Basis As a evision is linked to exactly one autho it is now possible to sum up all points of evey evision an autho has witten. This is done by using two methods to weight the esult: the length of the edit and the ceation time of a evision. 6 Evey change made to an aticle esults in a new evision. 7 These examples use a scale fom 0 (wost) to 5 (best) points, but it can be defined as wanted.
A modified set of R is ceated whee ik is evision k of aticle i (equation 4). R i {,,, K, }. = (4) i1 i2 i3 We assume that the moe diffeent a new evision is compaed to the fome one, the moe impotant wee the changes made. It does not matte, if a new evision is extended o shotened a suplus in content quality is assumed 8. The diffeence fom one evision to anothe is counted in bytes. Using equation 5 we get an oveall value of size changes fom all evisions k fom an aticle i (whee s ik is the specific size change fom one evision to the fome one). S i = n k= 1 abs [ size( ) size( )]. ik ik i( k 1) 14444 244443 (5) s ik The second assumption is that newe evisions count moe than olde ones. We believe that newe evisions have up-to-date topics and theefoe should be weighted highe than ones witten long ago. Equation 6 sums up the elative amount of time fo all evisions k fo an aticle i (t ik is the elative amount of time fo one evision). T i = n k = 1 time ( ) fisttime( ). ik i 1444 24444 3 (6) t ik Fo all evisions of evey aticle, the diffeentiation to the fome evision, and its age accoding to the ceation date of the aticle ae saved (equation 5 and 6). Equation 7 defines a subset A of evisions belonging to one autho. This means that only evisions fom the specific autho fo whom the calculation takes place ae consideed. So, fo example, s aik (in equation 8) is the size change fom one evision of the autho which is divided by the oveall size change of all evisions of the aticle to get a pecentage value. A R. (7) In equation 8 fo evey evision belonging to an autho and evey citeion (size and time) pecentage values ae geneated which ae weighted using a pedefined scale (w S and w T ). Then these two values ae multiplied with the specific points calculated in the fist step fo this evision (p k ) and both values ae summed up. The outcome is a new weighted value fo evey evision (p aik ) which has to be summed up fo all aticles belonging to an autho (p A ). p A = s n m aik aik * ws * p + k i= 1 k= 1 S T i i 14444 2 p a ik t * w T * p 44444 3 k. (8) 8 That means we facto out flames, tolls etc.
This pocedue has to be done fo all authos, so that in the end evey autho has one value assigned which is the basis fo displaying the anking. Fig. 1 gives an oveview of the two-step calculation pocess descibed in this section. Amount of Refeences Rating of Aticles Most Viewed Aticles Revision Basis Time Size Autho Basis Fig. 1. Two-step calculation pocess. At fist points ae computed on a evision basis using the thee descibed social ewading methods. In a second step the points ae weighted accoding to time and size factos and summed up fo an autho. Fig. 2. Sceenshot of anking of authos. Besides the authos names, stas and spaklines can be seen. The numbes on the ight ae the achieved scoes accoding to the calculation of the social ewading methods. 5 Ranking of Authos Fo displaying esults, vaious authos ankings can be geneated whee the most active one will see he-/himself on the fist place (Fig. 2). To suppot shown esults, two well-known data visualization techniques ae used: stas and spaklines [19]. 5.1 Stas Using stas to geneate a anking is well known and an established way to give a quick indication on how good o bad something is. Lage Intenet sites, like ebay o Amazon and many foum applications use stas as gaphical expessions. We ecommend using a five sta scaling to show the paticipation ate of a use (displaying half-stas can be activated additionally). As stas ae computed on the basis of the paticipation ate of all othe membes of the community, they ae a good indication fo the oveall contibution ate of a use. 5.2 Spaklines Spaklines ae small, high-esolution gaphics embedded in a context of wods, numbes, images. Spaklines ae data-intense, design-simple, wod-sized gaphics.
Spaklines have obvious applications fo financial and economic data, by tacking changes ove time, showing oveall tend as well as local detail [19]. In this wok spaklines ae used to show the paticipation ate of a use ove a cetain peiod of time split by pedefined intevals. Theefoe, the contibution ate is calculated using the thee social ewading mechanisms descibed ealie. We have chosen spaklines mainly because of thei good integation in a context of wods and thei simplicity. The appeaance, intevals, heights, widths, spaces, and colous of the spaklines can be customized by the use. 6 Implementation We implemented ou developed social ewading techniques as an extension in the MediaWiki system. Fo setting up the extension a configuation file is used whee all vaiables belonging to ou package can be configued (~100). As the computation of the authos anking depends stongly on the amount of aticles, evisions, and authos it can be vey time consuming. Theefoe, a caching algoithm was implemented so that the calculation does not have to be done upon evey single equest. Caching data can eithe be saved on the file system o in the database. By selecting the latte a histoy of authos anking can be geneated. Most functions of the extension wee implemented to be displayed as so-called SpecialPages. But also some self-defined makups can be inseted into an aticle to display infomation povided by ou package. At last, hooks ae used fo collecting necessay data fo the computation pocess. 7 Conclusion and Futue Wok Because unde-contibution is a seious poblem fo many online communities, we have tied in this pape to give an insight on how to motivate uses by means of social ewading techniques. We based ou wok on the accentuation of most active membes in a wiki. To find these uses we geneated an authos anking by making use of calculated points of thee developed social ewading mechanisms: Amount of Refeences, Rating of Aticles, and Most Viewed Aticles. Seveal weighting vaiables influence the anking. Some of them ae configuable; othes ely on the quantity, quality, and novelty of the authos text. Besides the anking, stas and spaklines ae used to visualize the esults. As an implementation platfom we have chosen MediaWiki in which ou social ewading mechanisms whee integated. Ou appoach can be seen as a stating point to develop mechanisms to the impotant issue of motivating uses to paticipate actively in a wiki system. In no othe online community the paticipation ate of uses is moe impotant than in a wiki, because thee poduces and consumes of the good (namely infomation) ae the same. If too less uses poduce content and only fee ide a wiki community will cannibalize itself. We think that ou implementation of social ewading techniques as a mixtue of seveal methods is a good way to ceate qualitative high esults which ae necessay to geneate non-monetay incentives fo uses. Nevetheless, it is a failue
to think that mechanisms we have descibed in this pape will be sufficient to motivate enough people to fom an active community to paticipate in evey wiki. Uses must have an intinsic motivation to contibute to a wiki which with ou developed techniques can only be stimulated. Ou poject is not publicly eleased yet and theefoe empiical data is not available. Fo this eason, we ae cuently planning to evaluate ou implemented concepts in a lage setting. Refeences 1. Ada, Eytan; Hubeman, Benado A.: Fee Riding on Gnutella. Xeox Palo Alto Reseach Cente (2000). 2. Chesney, Thomas: An Empiical Examination of Wikipedia s Cedibility. Fist Monday, volume 11, numbe 11 (2006). 3. Cones, Richad; Sandle, Todd: The Theoy of Extenalities, Public Goods and Club Goods. 2 nd ed. Cambidge Univ. Pess (1996). 4. Cosley, Dan; Fankowski, Dan; Kiesle, Saa; et. al.: How Ovesight Impoves Membe- Maintained Communities. CommunityLab (2005). 5. Ebesbach, Anna; Glase, Makus; Heigl, Richad: Wiki: Web Collaboation. Spinge, Belin Heidelbeg New Yok (2005). 6. Feldman, Michal; Papadimitiou, Chistos; Chuang, John; et. al.: Fee-Riding and Whitewashing in Pee-to-Pee Systems. School of Infomation Management and Systems, UC Bekeley (2004). 7. Google Technology: http://www.google.com/technology/, etieved on 2007-02-15. 8. Hoisl, Benhad: Social Rewading in Online Communities - A Focus on Wiki Systems. Maste thesis, to be published. Vienna Univesity of Technology (2007). 9. Jackson, J. M.; Hakins, S. G.: Equity in Effot: An Explanation of the Social Loafing Effect. In: Jounal of Pesonality and Social Psychology, volume 49, 1199-1206 (1985). 10. Kimbely, Ling; Beenen, Gead; Ludfod, Pamela; et. al.: Using Social Psychology to Motivate Contibutions to Online Communities. CommunityLab (2004). 11. Leuf, Bo; Cunningham, Wad: The Wiki Way: Quick Collaboation on the Web. Addison- Wesley Pofessional (2001). 12. Ludfod, Pamela; Cosley, Dan; Fankowski, Dan; et. al.: Think Diffeent: Inceasing Online Community Paticipation Using Uniqueness and Goup Dissimilaity. Univesity of Minnesota, Depatment of Compute Science and Engineeing (2004). 13. Maslow, Abaham H.: Motivation and Pesonality. 3 d ed. HapeCollins Publishes (1987). 14. McFedeies, Paul: Technically Speaking: It s A Wiki, Wiki Wold. In: IEEE Spectum, volume 43, 88 (2006). 15. Resnick, Paul; Zeckhause, Richad; Fiedman, Eic; et. al.: Reputation Systems: Facilitating Tust in Intenet Inteactions. Communications of the ACM, volume 43, issue 12, 45-48 (2000). 16. Sange, Lay: Why Wikipedia Must Jettison Its Anti-Elitism. http://www.kuo5hin.og/stoy/2004/12/30/142458/25, etieved on 2007-02-15. Kuo5hin (2004). 17. Schobeth, Thomas; Peece, Jenny; Heinzl, Amin: Online Communities: A Longitude Analysis of Communication Activities. Woking Pape in Infomation Systems (2003). 18. Tapscott, Don; Williams, Anthony D.: Wikinomics: How Mass Collaboation Changes Eveything. Potfolio (2006). 19. Tufte, Edwad R.: Beautiful Evidence. Gaphics Pess, 7 19 (2006).