Breaking the News: First Impressions Matter on Online News
Table 3: Distribution of news gathered across category
['BBC News Category', 'BBC News %', 'Dailymail Category', 'Dailymail %', 'New York Times Category', 'New York Times %', 'Reuters Category', 'Reuters %']
In order to infer the topical categories of the news articles, we use meta information embedded in the news URLs. For example, the URL http://www.nytimes.com/2014/03/29/sports/ncaabasketball/once-again-izzo-is-one-step-away.html is categorized as “Sports”. BBC, Dailymail and The New York Times adopt such a strategy in their URLs, and thus it was possible for us to parse their URL to infer the topic of their news articles. For Reuters, the news was grouped by category in the archive from which we collected the data. So we took the topic from the category name. In total, only 9% of the news items from this online newspaper could not be identified and was discarded for this analysis. We have standardized the names of some categories to simplify the comparison among news sources. We note that World news is the most representative category in all news sources. In this section, we look into the sentiment scores of news headlines across different categories. First, observe that different categories have different distributions of sentiment scores. Health and World are predominantly negative for all four news sources. Except for Dailymail, the Science and Technology category tends to have more neutral news headlines, but still the amount of negative ones is higher than the positive ones. For instance, the categories “Dining” and “Travel” of The New York Times have essentially positive headlines as they are dedicated to review and suggest places to go. Next, we investigate if the overall trend toward negative news headlines is consistent over time.