# Collecting a Large Scale Dataset for Classifying Fake News Tweets Using Weak Supervision
## Appendix A Features

Table 10: Overview of the derived user level features. \begin{tabular}{l l} \hline \hline
**Feature Name** & **Description** \\ \hline contains\_number & If true, the tweet contains a token that is a numerical \\ \hline contains\_quote & If true, there is a quoted text in the tweet \\ \hline day\_of\_week & The day of the week the tweet was created \\ \hline day\_of\_month & The day of the month the tweet was created \\ \hline month\_of\_year & The month the tweet was created \\ \hline am\_pm & If true, the tweet was published after noon. If present, the users UTC offset was \\ \hline hour\_of\_day & The hour the tweet was published in the 24 h format. If present, the users UTC offset was \\ \hline quarter\_of\_year & The quarter of the year the tweet was published \\ \hline no\_text & If true, a tweet does not contain text. This is possible when the status update is an URL \\ \hline avg\_word\_length & The average word length in the tweet. URLs are not counted. \\ text\_length & The length of the text according to the Twitter character counting. User mentions are not counted. The feature is based on the raw text. \\ \hline percent\_of\_text\_used & The ratio of used characters to the 140 possible characters. \\ \hline nr\_of\_words & Counts the words a in tweet. This feature is based on preprocessing stage two. \\ \hline nr\_of\_tokens & Counts the number of tokens that were generated by the tokenizer. It considers \\ \hline ratio\_words\_tokens & States the ratio between words and tokens. \\ \hline ratio\_uppercase\_letters & The ratio of uppercase letters to all cased characters in the tweet's text \\ \hline is\_all\_uppercase & If true, all characters in the raw text are uppercase \\ contains\_uppercase\_text & If true, the tweet contains a sequence of characters of length five or larger that contain \\ \hline ratio\_capitalized\_words & Counts the number of capitalized words and relates them to the word count. Words with only capitalized characters are not included in the count for capitalized words. \\ \hline ratio\_all\_capitalized\_words & Ratio of words that contain only capitalized letters to the total number of words. It is \\ \hline nr\_of\_sentences & Counts the number of sentences. If there is only one sentence, this does not necessarily mean that this is a complete sentence. Preprocessing stage seven refers to this feature. \\ \hline contains\_character & If true, a character is followed by at least two repetitions of itself in the raw text from stage \\ \hline nr\_of\_lang\_words & The number of slang words in the tweet from the slang word dictionary. Preprocessing stage two was used here. \\ \hline contains\_slang & If true, the tweet contains at least one slang word \\ \hline ratio\_adjectives & The ratio of adjectives to the total number of words after POS tagging in stage four \\ \hline ratio\_nouns & The ratio of nouns to the total number of words after POS tagging in stage four \\ \hline ratio\_verbs & Ratio of verbs to the total number of words after POS tagging in stage four \\ \hline contains\_named\_entity & If true, a token of the tweet was tagged as a named entity after POS tagging in stage four \\ \hline contains\_pronouns & If true, a token of the tweet was tagged as a pronoun after POS tagging in stage four \\ \hline additional\_preprocessed & If true, after all text preprocessing steps in stage six no tokens are left \\ \hline ratio\_tokens\_before\_after & The ratio of the number of tokens before preprocessing (stage three) to the number of tokens afterwards (stage six). Can be greater than one since abbreviations are resolved after tokenization. \\ \hline ratio\_stopwords & The ratio of the tokens from preprocessing stage six where stopwords were removed to \\ \hline contains\_spelling\_mistake & If true, a spelling mistake was found through spell checking \\ \hline \hline \end{tabular}