Predicting Human Activities from User-Generated Content
Table 3: Summary of query results.
['Total queries', '29,494']
We then filter the queried tweets as follows: first, we verify that for any tweets requiring the match of multiple substrings (due to wildcards in the original activity phrase), the substrings appear in the correct order and do not span multiple sentences. Next, we remove activity phrases that are preceded with indications that the author of the tweet did not actually perform the activity, such as “I wish” or “should I …?”. We refer to the set of tweets left after this filtering as valid queried tweets (see