One Million Posts

Schabus, Dietmar; Skowron, Marcin; Trapp, Martin

doi:10.1145/3077136.3080711

Cited by 24 publications

(15 citation statements)

References 8 publications

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“…First, we empirically explore and evaluate the solution space for this classification task based on supervised and endto-end machine learning approaches with respective hyperparameter optimization. Second, we propose a neural network model for the end-to-end learning which outperforms state-of-the-art comment classification reported by Schabus et al [56]. Third, we give insights into designing comment analytics tools and use cases for the information extracted from meta-comments.The remainder of the paper is structured as follows.…”

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confidence: 89%

“…To answer our research questions, we used two datasets: (1) user comments posted on SPIEGEL Online 1 (SPON) and (2) the "One Million Posts" (OMP) corpus [56]. We selected the SPON news page for two reasons.…”

Section: Research Datamentioning

confidence: 99%

“…Additionally, we used the partly annotated comments of OMP, a dataset that consists of 11,773 labeled and one million unlabeled German online user comments posted on DER STANDARD, an Austrian newspaper website. The authors define the annotation category "feedback" as: "Sometimes users ask questions or give feedback to the author of the article or the newspaper in general, which may require a reply/reaction" [56]. This description is equivalent to our meta-comment definition.…”

Section: Research Datamentioning

confidence: 99%

“…We describe only the SPON dataset as Schabus et al [56] report on the OMP dataset in-depth. The number of SPON user comments per year has steadily increased from 2005 to 2011 from 0.1 million to 1.6 million.…”

Section: Quantitative Content Analysismentioning

confidence: 99%

“…We used three different word embedding models for the end-to-end learning approach. Table 2 compares our generated SPON model with two other models: the OMP model according to Schabus et al [56], and the GermanWord model that Müller [44] trained on German Wikipedia and news articles.…”

Section: Training Word and Comment Embeddingsmentioning

confidence: 99%

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Who is Addressed in this Comment?

Häring

Loosen

Maalej

2018

Proc. ACM Hum.-Comput. Interact.

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User comments have become an essential part of online journalism. However, newsrooms are often overwhelmed by the vast number of diverse comments, for which a manual analysis is barely feasible. Identifying meta-comments that address or mention newsrooms, individual journalists, or moderators and that may call for reactions is particularly critical. In this paper, we present an automated approach to identify and classify meta-comments. We compare comment classification based on manually extracted features with an end-to-end learning approach. We develop, optimize, and evaluate multiple classifiers on a comment dataset of the large German online newsroom SPIEGEL Online and the "One Million Posts" corpus of DER STANDARD, an Austrian newspaper. Both optimized classification approaches achieved encouraging F 0.5 values between 76% and 91%. We report on the most significant classification features with the results of a qualitative analysis and discuss how our work contributes to making participation in online journalism more constructive. 67:2 M. Häring et al.and newsrooms to analyze, filter, and summarize user comments has been identified as a primary challenge for news organizations [19][20][21].Research has shown that most journalists have a clear sense of what they deem useful user contributions [34]. For instance, journalists particularly appreciate user feedback that reports errors in articles, include additional information on a topic, or contain critique addressed to the quality of an article. Media companies can use this information to improve journalistic work, correct articles, answer frequent questions, or gather feedback on the quality of their news coverage.A previous study by Loosen et al. [34] demonstrated, through group discussions with journalists and community-moderators, that the prospect of a software system for analyzing user comments was highly welcomed. One feature journalists considered particularly useful is the ability to identify the addressee in comments, for example, the newsroom or media organization, the author of the article being commented on, actors mentioned in the article, or other actors and users. This would help to direct comments to the newsroom or to single journalists that may call for reactions as correcting facts, answering questions, or providing additional information. This is all the more the case as it is also likely that user comments that address the author or the newsroom directly contain elements of media critique or praise [17].Our work aims to develop and evaluate an approach to automatically identify and classify user comments based on whom they address. We focus on comments that are not (only) related to the article but address, for instance, the media company, a journalist, or a community-moderator. We call these comments "meta-comments". The contribution of this paper is threefold. First, we empirically explore and evaluate the solution space for this classification task based on supervised and endto-end machine learning approaches with respective hyperparame...

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confidence: 89%

Section: Research Datamentioning

confidence: 99%

Section: Research Datamentioning

confidence: 99%

Section: Quantitative Content Analysismentioning

confidence: 99%

Section: Training Word and Comment Embeddingsmentioning

confidence: 99%

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