A Computational Analysis of Polarization on Indian and Pakistani Social Media
Table 1: Classification results for the 100 most followed Indian and Pakistan Twitter accounts, where Pro-India or Pro-Pakistan are treated as the dominant class, and the nationality of the account owner is treated as a gold label. Accounts that our algorithm was unable to classify (% Unk) are not included in precision and recall scores.
['[EMPTY]', 'Pro-India Prec.', 'Pro-India Recall', 'Pro-India % Unk.', 'Pro-Pakistan Prec.', 'Pro-Pakistan Recall', 'Pro-Pakistan % Unk.']
Both of the network-based approaches rely on hashtag or word co-occurrences to propagate labels. Thus, hashtags and words that do not have any co-occurrence links to the original seed list are unable to be labeled. For instance, in the hashtag propagation approach, our method labels 41,700 hashtags out of 67,059 total hashtags in the dataset. In contrast, SentProp obtains polarity scores for all accounts, as it relies on embedding similarity and can propagate labels between words, even if they do not ever co-occur.