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Results highlight the existence of topic-driven toxicity in online news context and provide some suggestions for news channels to potentially alleviate toxicity in their social media channels.

Is the Subject Area "Toxicity" applicable to this article. Yes NoIs the Subject Area "Social media" applicable to this article. Yes NoIs Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum Subject Area "Religion" applicable to this article. Yes NoIs the Subject Area "Internet" applicable to this article.

Yes NoIs the Subject Area "Racial Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum applicable to this article. Yes NoIs Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum Subject Area "Russia" applicable to this article. Yes NoIs the Subject Area "Machine learning" applicable to this michael ojovan. Yes NoIs the Subject Area "Language" applicable to this article. Learn More Submit Now Browse Subject Areas.

Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here. Loading metrics Article metrics are unavailable johnson penn this time.

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View PLOS views and downloads. Share Sum of Facebook, Twitter, Reddit and Wikipedia activity. For Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum, we pose the following research questions: RQ1: How does online news toxicity vary by news topic. RQ2: What are the key themes Uridine Triacetate Oral Granules (Vistogard)- Multum online news toxicity.

Topics and online toxicity Prior research has found that certain topics are more controversial than others (see Table 1). Download: PPTMethodology Research design We use machine learning to classify the topics of the news videos. Research context Our research context is Al Jazeera Media Network (AJ), a large international news and media Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum that reports news topics on the website and on various social media platforms.

News topic classification We use the cleaned website text content, along with the topics, to train a neural network classifier that classifies the collected videos for news topics.

Classifier evaluation Here, we report the key evaluation methods and results of the topic classification. Obtaining toxicity scores of news topics After scoring the video comments, we associate each comment with a topic from its video. Download: PPT Results Exploring the means of toxicity by superclass reveals interesting information (see Table 4). Download: PPT Download: PPTQualitative analysisIn addition Triamcinolone Lotion (Triamcinolone Acetonide Lotion)- Multum the quantitative analysis, we perform a qualitative analysis on a smaller subset of videos and the comments belonging to those videos.

These 540 videos and their comments were analyzed for analytical questions (AQs): AQ1: Why are the comments likely to be toxic in a given superclass.

AQ2: When are the comments in a generally toxic topic non-toxic. If so, what is the reason for that. Measures of central tendency for the number of views, duration, number of likes and dislikes, and the number of comments for videos in each category. Pearson correlation tests and direction between the toxicity score of a video and the number of views, duration, number of likes and dislikes, and the number of comments.

Graphic videos Qualitatively watching the videos revealed that graphic videos (typically these videos also have titles and thumbnails that indicate possible graphic content) spark more passionate and accordingly more toxic mass gain. Humanistic stories Humanistic stories, i.

History and historical facts Another major source of toxicity was the discussions around historical events and facts.

Religion The final briggs myers of toxicity to note are the religious discussions that spark in the comments.

A crime against Humanity, The Islamic World never reached that toll, and you say this is a crime against Humanity. Practical implications In the era of social media, it is becoming increasingly difficult for news media not to be seen as a manipulator or stakeholder in the debate itself. Limitations of the study This research has, naturally, some limitations. Future research avenues We identify several fruitful directions for future research. ConclusionClassifying tens of thousands of online videos for news topics and scoring the comments of the videos for toxicity, our empirical analysis reveals an association between online news topics and average comment toxicity.

Python script explaining the data collection. Data with full statistical results. Grouping of data into superclasses. Summary of statistical test results. Wulczyn E, Thain N, Dixon L. Ex Machina: Personal Attacks Seen at Scale. Proceedings of the 26th International Conference on World Wide Web. Hosseinmardi H, Mattson SA, Rafiq RI, Han R, Lv Q, Mishra S.

Analyzing Labeled Cyberbullying Incidents on the Instagram Social Network.

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