Predicting Human Activities from User-Generated Content
Table 4: Summary of additional data.
['Num. unique users', '358,091']
Further, we use a simple pattern-matching approach to extract additional activities from these additional tweets. We search for strings that match I   . *   where   is any past-tense verb, . * matches any string (non-greedy), and   matches the end of a sentence. We then perform the same filtering as before for indications that the person did not actually do the activity, and we refer to these filtered matches as our set of additional activities (see for more information). Note that since these additional activities can contain any range of verbs, they are naturally noisier than our set of valid query tweets, and we therefore do not treat them as a reliable “ground truth” source of self-reported human activities, but as a potentially useful signal of activity-related information that can be associated with users in our dataset.