Using New York Times Picks to Identify Constructive Comments

Kolhatkar, Varada; Taboada, Maïté

doi:10.18653/v1/w17-4218

Cited by 21 publications

(11 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Park et al (2016) develop a system, CommentIQ, which supports comment moderators in interactively identifying high quality comments. Kolhatkar and Taboada (2017) also proposes a model to classifier the comments, and they focuses on the constructive comments, which is different from ours.…”

Section: Related Workmentioning

confidence: 98%

Predicting Popular News Comments Based on Multi-Target Text Matching Model

Chen

Yang

et al. 2019

Natural Language Processing and Chinese Computing

View full text Add to dashboard Cite

With the development of information technology, there is an explosive growth in the number of online comment concerning news, blogs and so on. The massive comments are overloaded, and often contain some misleading and unwelcome information. Therefore, it is necessary to identify high-quality comments and filter out low-quality comments. In this work, we introduce a novel task: high-quality comment identification (HQCI), which aims to automatically assess the quality of online comments. First, we construct a news comment corpus, which consists of news, comments, and the corresponding quality label. Second, we analyze the dataset, and find the quality of comments can be measured in three aspects: informativeness, consistency, and novelty. Finally, we propose a novel multi-target text matching model, which can measure three aspects by referring to the news and surrounding comments. Experimental results show that our method can outperform various baselines by a large margin on the news dataset.

show abstract

Section: Related Workmentioning

confidence: 98%

Predicting Popular News Comments Based on Multi-Target Text Matching Model

Chen

Yang

et al. 2019

Natural Language Processing and Chinese Computing

View full text Add to dashboard Cite

show abstract

“…Similarly, Kolhatkar and Taboada [19] classify constructive comments. They use editor picks from New York Times comments as positive examples, and comments from non-constructive threads from the Yahoo News Annotated Comments Corpus as negative examples to train their bidirectional, long short-term memory (LSTM) model.…”

Section: Related Workmentioning

confidence: 98%

A Dataset of Journalists' Interactions with Their Readership

Risch

Krestel

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

The comment sections of online news platforms are an important space to indulge in political conversations and to discuss opinions. Although primarily meant as forums where readers discuss amongst each other, they can also spark a dialog with the journalists who authored the article. A small but important fraction of comments address the journalists directly, e.g., with questions, recommendations for future topics, thanks and appreciation, or article corrections. However, the sheer number of comments makes it infeasible for journalists to follow discussions around their articles in extenso. A better understanding of this data could support journalists in gaining insights into their audience and fostering engaging and respectful discussions. To this end, we present a dataset of dialogs in which journalists of The Guardian replied to reader comments and identify the reasons why. Based on this data, we formulate the novel task of recommending reader comments to journalists that are worth reading or replying to, i.e., ranking comments in such a way that the top comments are most likely to require the journalists' reaction. As a baseline, we trained a neural network model with the help of a pair-wise comment ranking task. Our experiment reveals the challenges of this task and we outline promising paths for future work. The data and our code are available for research purposes from: https://hpi.de/naumann/projects/repeatability/text-mining.html. CCS CONCEPTS • Information systems → Recommender systems; • Computing methodologies → Language resources; Discourse, dialogue and pragmatics; • Human-centered computing → Social media.

show abstract

“…The SciCAR conferences 7 and the recent ''Natural Language Processing meets Journalism'' workshops (Birnbaum et al 2016;Popescu andStrapparava 2017, 2018) are predominant examples for this development. Previous research focuses on news headline generation and click-bait analysis (Blom and Hansen 2015;Gatti et al 2016;Szymanski et al 2016), abusive language and comment moderation (Clarke and Grieve 2017;Kolhatkar and Taboada 2017;Pavlopoulos et al 2017;Schmidt and Wiegand 2017), news bias and filter bubble analyses (Baumer et al 2015;Bozdag and van den Hoven 2015;Fu et al 2016;Kuang and Davison 2016;Potash et al 2017), as well as news verification and fake news detection (Brandtzaeg et al 2015;Thorne et al 2017;Bourgonje et al 2017;Hanselowski et al 2018;Thorne et al 2018). We are not aware of any work on live blog summarization or computational approaches closely related to journalistic live blogging.…”

Section: Nlp and Journalismmentioning

confidence: 99%

Live blog summarization

Avinesh

Peyrard

Meyer

2021

Lang Resources & Evaluation

View full text Add to dashboard Cite

Live blogs are an increasingly popular news format to cover breaking news and live events in online journalism. Online news websites around the world are using this medium to give their readers a minute by minute update on an event. Good summaries enhance the value of the live blogs for a reader, but are often not available. In this article, (a) we first define the task of summarizing a live blog, (b) study ways of automatically collecting corpora for live blog summarization, and (c) understand the complexity of the task by empirically evaluating well-known state-of-the-art unsupervised and supervised summarization systems on our new corpus. We show that live blog summarization poses new challenges in the field of news summarization, since frequency and positional signals cannot be used. We make our tools publicly available to reconstruct the corpus and to conduct our empirical experiments. This encourages the research community to build upon and replicate our results.

show abstract

Using New York Times Picks to Identify Constructive Comments

Cited by 21 publications

References 12 publications

Predicting Popular News Comments Based on Multi-Target Text Matching Model

Predicting Popular News Comments Based on Multi-Target Text Matching Model

A Dataset of Journalists' Interactions with Their Readership

Live blog summarization

Contact Info

Product

Resources

About