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Appendix A BIGNews Cleaning Steps
In this section, we provide the details of our data cleaning steps for BIGNews. We adopt the following cleaning steps to only keep news articles that relate to US politics.
Removing Non-article Pages. Online news websites also post non-news content. We remove such pages by checking their page titles and URLs based on a list of patterns. Sample patterns are shown in Table A1.
Removing Duplicate Pages. We use character-level edit distance to identify duplicate pages. Specifically, we use the following formula to calculate the difference between page  $a$  and page  $b$ :
$$
\operatorname {d i f f} (a, b) = \operatorname {d i s t} (a, b) / \max  (\operatorname {l e n} (a), \operatorname {l e n} (b)) \tag {5}
$$
where  $\mathrm{dist}(a,b)$  is the Levenshtein distance between  $a$  and  $b$ . If the value is less than 0.1, we consider two pages as duplicates and we only keep the one with earlier publication date. Following this procedure, we remove duplicated pages within each media outlet.
Filter Patterns
URL	/video/, /gallery/, /slidesshow/
Title	weekly digest, 10 sites you should know, day's end roundup, photos of the week, 5 things you need to know
Removing Non-politics Pages. To filter out non-politics pages, we build a classifier using training data from BIGNews. Since URL typically indicates a page's topic, we use keywords in the URL to retrieve politics and non-politics training data. The lists of keywords are shown in Table A2. This results in a training dataset with 400,462 politics pages and 310,377 non-politics pages. We also randomly sample 888 pages from the remaining dataset and manually annotate them to use as the test set.
We train a logistic regression model based on unigram and bigram TF-IDF features. To include pages not covered by the lists of keywords in Table A2, we use the trained classifier to classify remaining pages and add those classified with high
Table A1: Examples of patterns used to filter out pages that are not news stories.
Keywords
Politics	/politics/,/political/,/policy/,/election/,/elections/,/allpolitics/
Non-politics	/travel/,/sports/,/life/,/movie/,entertainment/,/science/,/music/,plated/,/leisure/,/showbiz/,/lifestyle/,/fashion/,/art/,/sport/
Table A2: Full list of keywords used to retrieve positive and negative training data for the politics classifier.
URL Keywords	Text Keywords
/world/, /international/, /europe/, /africa/, /asia/, /latin-america/, /middle-east/	U.S., United States, Obama, Trump, Bush, Biden, Pompeo, Clinton, Pence
Table A3: Examples of keywords used to filter out non-US articles. For text keywords, we collect the names of all presidents, vice presidents, and secretaries of state of US since 2000.
probability to the training data. This results in a larger training set with 957,424 politics pages and 987,898 non-politics pages. We train the final classifier on the larger training set, and achieve an  $88.18\%$  accuracy on the test data.
Removing Non-US Pages. We filter out pages that are not related to US by searching for non-US keywords in the URL. For each of those pages, we only remove it if its text contains no US-related keywords. Examples of keywords used are shown in Table A3.
Removing Media Leaking Phrases. To prevent the model from learning features specific to individual media outlets, we perform a two-step cleaning. First, we mask phrases that mention the media outlet itself (e.g., New York Times, NYTimes, and nytimes.com). Second, we create a list of patterns for frequently appearing sentences (more than 100 times), for each media outlet. For example, as in "author currently serves as a senior political analyst for [MASK] Channel and contributes to all major political coverage", both the author name and the sentence itself can leak media outlet information. Since sentences with media leaking information usually appear at the beginning or end of the article, we remove any of the first and last two