What influences online deliberation? A wikipedia study

Xiao, Lu; Askin, Nicole

doi:10.1002/asi.23004

Cited by 25 publications

(22 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A differentiating feature of the voter in the previous example is the citation of relevant and targeted policy, Notability (geographic features). In prior work, this citation behavior was previously noted as highly relevant for further study [65]; in particular, they noted that locations, biographies, and corporate pages were deleted at substantially different rates compared to pages in general. Our research extends that finding: Notability policies are among the most informative votes in our forecast model, appear early in debates (particularly often in Keep votes), and are more successful in general than other policies and more than votes in general.…”

Section: Notability Policiesmentioning

confidence: 71%

“…Along with measuring any particular user behavior, like arriving early or posting frequently, we will also single out specific policies that are often cited in exemplar cases of that behavior. This is a useful analytic lens; policies have consistently been a focus area of AfD research, from the close study of the Ignore All Rules policy in [33] to the study of notability subpolicies in [65]. The broad theme of our findings is that the relationship between policy citation, success, and forecast shift is nuanced.…”

Section: Analysis With the Forecast Modelmentioning

confidence: 89%

“…More recently beginning in 2014 with [65], Xiao et al undertook a series of mixed-methods studies of rationales in AfD votes. They again replicated the finding that vote counts did significantly predict outcomes.…”

Section: Prior Workmentioning

confidence: 99%

See 2 more Smart Citations

Analyzing Wikipedia Deletion Debates with a Group Decision-Making Forecast Model

Mayfield

Black

2019

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

In this work we show that machine learning with natural language processing can accurately forecast the outcomes of group decision-making in online discussions. Specifically, we study Articles for Deletion, a Wikipedia forum for determining which content should be included on the site. Applying this model, we replicate several findings from prior work on the factors that predict debate outcomes; we then extend this prior work and present new avenues for study, particularly in the use of policy citation during discussion. Alongside these findings, we introduce a structured corpus and source code for analyzing over 400,000 deletion debates spanning Wikipedia's history, enabling future large-scale studies of group decision-making discourse. CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing; • Computing methodologies → Machine learning; • Information systems → World Wide Web.

show abstract

Section: Notability Policiesmentioning

confidence: 71%

Section: Analysis With the Forecast Modelmentioning

confidence: 89%

See 1 more Smart Citation

Analyzing Wikipedia Deletion Debates with a Group Decision-Making Forecast Model

Mayfield

Black

2019

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

show abstract

“…From an argumentation study perspective, offering one's rationales has the power to persuade others and to bolster one's credibility (Aristotle, ) and promote one's and peers’ reflective thinking process (Xiao & Carroll, ). Research on online deliberations indicated the importance of understanding the shared rationales in these activities in collective decision‐making (Graham & Witschge, ; Xiao & Askin, ).…”

Section: Introductionmentioning

confidence: 99%

Discourse relations in rationale‐containing text‐segments

Xiao

Conroy

2017

Asso for Info Science & Tech

Self Cite

View full text Add to dashboard Cite

Offering one's perspective and justifying it has become a common practice in online text-based communications, just as it is in typical, face-to-face communication.Compared to the face-to-face communications, it can be particularly more challenging for users to understand and evaluate another's perspective in online communications. On the other hand, the availability of the communication record in online communications offers a potential to leverage computational techniques to automatically detect user opinions and rationales. One promising approach to automatically detect the rationales is to detect the common discourse relations in rationale texts. However, no empirical work has been done with regard to which discourse relations are commonly present in the users' rationales in online communications. To fill this gap, we annotated the discourse relations in the text segments that contain the rationales (N 5 527 text segments). These text segments are obtained from five datasets that consist of five online posts and the first 100 comments. We identified 10 discourse relations that are commonly present in this sample. Our finding marks an important contribution to this rationale detection approach. We encourage more empirical work, preferably with a larger sample, to examine the generalizability of our findings.

show abstract

“…Schneider et al (2013) investigated the difference in arguments from novices and experienced users. Xiao and Askin (2014) examined the types of rationales in Wikipedia AfD discussions.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the First Workshop on Argumentation Mining

Lawrence¹,

Reed²,

Allen³

et al. 2014

View full text Add to dashboard Cite

ii Introduction Argumentation mining is a relatively new challenge in corpus-based discourse analysis that involves automatically identifying argumentative structures within a document, e.g. the premises, conclusion, and argumentation scheme of each argument, as well as argument-subargument and argumentcounterargument relationships between pairs of arguments. Proposed applications of argumentation mining include improving information retrieval and information extraction, as well as providing end-user visualization and summarization of arguments. Sources of interest include not only formal genres, but also a variety of informal genres such as microtext, spoken meeting transcripts, and product reviews. In instructional contexts where argumentation is a pedagogically important tool for conveying and assessing students' command of course material, the written and diagrammed arguments of students (and the mappings between them) are educational data that can be mined for purposes of assessment and instruction. This is especially important given the wide-spread adoption of computer-supported peer review, computerized essay grading, and large-scale online courses and MOOCs.Success in argumentation mining will require interdisciplinary approaches informed by natural language processing technology, theories of semantics, pragmatics and discourse, knowledge of discourse of domains such as law and science, artificial intelligence, argumentation theory, and computational models of argumentation. In addition, it will require creation and annotation of high-quality corpora of argumentation from different types of sources in different domains.The goal of this workshop is to provide the first research forum devoted to argumentation mining in all domains of discourse. Suggested topics include but are not limited to:• Automatic identification of argument elements (e.g., premises and conclusion; data, claim and warrant), argumentation schemes, relationships between arguments in a document, and relationships to discourse goals (e.g. stages of a "critical discussion") and/or rhetorical strategies;• Creation/evaluation of argument annotation schemes, relationship of argument annotation to linguistic and discourse structure annotation schemes, (semi)automatic argument annotation methods and tools, and creation/annotation of high-quality shared argumentation corpora;• Processing strategies integrating NLP methods and AI models developed for argumentation such as argumentation frameworks; and• Applications of argument/argumentation mining to, e.g., mining requirements and technical documents, analysis of arguments in dialogue (meetings, etc.), opinion analysis and mining consumer reviews, evaluation of students' written arguments and argument diagrams, and information access (retrieval, extraction, summarization, and visualization) in scientific and legal documents. AbstractAutomated argumentation mining requires an adequate type system or annotation scheme for classifying the patterns of argument that succeed or fail in a corpus of le...

show abstract

What influences online deliberation? A wikipedia study

Cited by 25 publications

References 40 publications

Analyzing Wikipedia Deletion Debates with a Group Decision-Making Forecast Model

Analyzing Wikipedia Deletion Debates with a Group Decision-Making Forecast Model

Discourse relations in rationale‐containing text‐segments

Proceedings of the First Workshop on Argumentation Mining

Contact Info

Product

Resources

About