Let x 1 be a tweet with the following words (the numbers after the colon are TF-IDF values): x 1 = {obama:5.5, medicare:8.3, website:3.8} which has two nearest neighbors: x 27 = {obama:5.5, medicare:8.3, website:3.8, down:5.4} x 356 = {obama:5.5, medicare:8.3, website:3.8, problem:7.0} Then there are two additional words added in x 1 whose values are averaged.
we chose a subset of hashtags from the most frequent hashtags to create groundtruth labels: we manually removed some tags from the subset that are not topic-related (e.g., #truth, #lol) or are ambiguous; we also removed all the tags that are referring to TV series (the relevant tweets can be trivially obtained by named entity matching).