In this paper, we describe our approach to the Wikipedia Participation Challenge which aims to predict the number of edits a Wikipedia editor will make in the next 5 months. The best submission from our team, "zeditor", achieved 41.7 improvement over WMF's baseline predictive model and the final rank of 3rd place among 96 teams. An interesting characteristic of our approach is that only temporal dynamics features (i.e., how the number of edits changes in recent periods, etc.) are used in a self-supervised learning framework, which makes it easy to be generalised to other application domains.
The global slowdown of Wikipedia's growth rate (both in the number of editors and the number of edits per month) has been studied @cite_8 . It is found that medium-frequency editors now cover a lower percentage of the total population while high frequency editors continue to increase the number of their edits. Moreover, there are increased patterns of conflict and dominance (e.g., greater resistance to new edits in particular those from occasional editors), which may be the consequence of the increasingly limited opportunities in making novel contributions. These findings could guide us to generate other kinds of useful features to tackle the problem of edit number prediction. Furthermore, researchers have also investigated other activities of Wikipedia's editors, such as voting on the promotion of Wikipedia admins @cite_9 . In addition to Wikipedia, the temporal dynamics of online users' behaviour has been explored and exploited in web search @cite_7 @cite_11 @cite_6 @cite_4 , social tagging @cite_3 @cite_5 , blogging @cite_15 , twittering @cite_13 , and collaborative filtering @cite_10 . The @cite_14 and the @cite_2 seem to be recurrent themes across application domains.
@cite_14 @cite_4 @cite_7 @cite_8 @cite_10 @cite_9 @cite_6 @cite_3 @cite_2 @cite_5 @cite_15 @cite_13 @cite_11
1993500013 2110618133 2252123752 2152667709
Prior research on Wikipedia has characterized the growth in content and editors as being fundamentally exponential in nature, extrapolating current trends into the future. We show that recent editing activity suggests that Wikipedia growth has slowed, and perhaps plateaued, indicating that it may have come against its limits to growth. We measure growth, population shifts, and patterns of editor and administrator activities, contrasting these against past results where possible. Both the rate of page growth and editor growth has declined. As growth has declined, there are indicators of increased coordination and overhead costs, exclusion of newcomers, and resistance to new edits. We discuss some possible explanations for these new developments in Wikipedia including decreased opportunities for sharing existing knowledge and increased bureaucratic stress on the socio-technical system itself. This paper investigates some of the social roles people play in the online community of Wikipedia. We start from qualitative comments posted on community oriented pages, wiki project memberships, and user talk pages in order to identify a sample of editors who represent four key roles: substantive experts, technical editors, vandal fighters, and social networkers. Patterns in edit histories and egocentric network visualizations suggest potential "structural signatures" that could be used as quantitative indicators of role adoption. Using simple metrics based on edit histories we compare two samples of Wikipedians: a collection of long term dedicated editors, and a cohort of editors from a one month window of new arrivals. According to these metrics, we find that the proportions of editor types in the new cohort are similar those observed in the sample of dedicated contributors. The number of new editors playing helpful roles in a single month's cohort nearly equal the number found in the dedicated sample. This suggests that informal socialization has the potential provide sufficient role related labor despite growth and change in Wikipedia. These results are preliminary, and we describe several ways that the method can be improved, including the expansion and refinement of role signatures and identification of other important social roles. In this paper, we analyze a novel set of features for the task of automatic edit category classification. Edit category classification assigns categories such as spelling error correction, paraphrase or vandalism to edits in a document. Our features are based on differences between two versions of a document including meta data, textual and language properties and markup. In a supervised machine learning experiment, we achieve a micro-averaged F1 score of .62 on a corpus of edits from the English Wikipedia. In this corpus, each edit has been multi-labeled according to a 21-category taxonomy. A model trained on the same data achieves state-of-the-art performance on the related task of fluency edit classification. We apply pattern mining to automatically labeled edits in the revision histories of different Wikipedia articles. Our results suggest that high-quality articles show a higher degree of homogeneity with respect to their collaboration patterns as compared to random articles. In this paper, we present a study of the collaborative writing process in Wikipedia. Our work is based on a corpus of 1,995 edits obtained from 891 article revisions in the English Wikipedia. We propose a 21-category classification scheme for edits based on Faigley and Witte’s (1981) model. Example edit categories include spelling error corrections and vandalism. In a manual multi-label annotation study with 3 annotators, we obtain an inter-annotator agreement of = 0.67. We further analyze the distribution of edit categories for distinct stages in the revision history of 10 featured and 10 non-featured articles. Our results show that the information content in featured articles tends to become more stable after their promotion. On the opposite, this is not true for non-featured articles. We make the resulting corpus and the annotation guidelines freely available. 1