TwitterMancer: Predicting Interactions on Twitter Accurately
This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions. For example, given a set of retweet interactions between Twitter users, how accurately can we predict reply interactions? Is it more difficult to predict retweet or quote interactions between a pair of accounts? Also, how important is time locality, and which features of interaction patterns are most important to enable accurate prediction of specific Twitter interactions? Our empirical study of Twitter interactions contributes initial answers to these questions. We have crawled an extensive dataset of Greek-speaking Twitter accounts and their follow, quote, retweet, reply interactions over a period of a month. We find we can accurately predict many interactions of Twitter users. Interestingly, the most predictive features vary with the user profiles, and are not the same across all users. For example, for a pair of users that interact with a large number of other Twitter users, we find that certain "higher-dimensional" triads, i.e., triads that involve multiple types of interactions, are very informative, whereas for less active Twitter users, certain in-degrees and out-degrees play a major role. Finally, we provide various other insights on Twitter user behavior. Our code and data are available at https://github.com/twittermancer/. Keywords: Graph mining, machine learning, social media, social networks
Code (0)
등록된 구현이 없습니다.
Tasks
Graph MiningSimilar Papers 제목 키워드 기반
Exploring Optimism and Pessimism in Twitter Using Deep Learning
Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need such support, and for minimizing the negative influence among users and maximizing …
Deep LearningPredicting the 2020 US Presidential Election with Twitter
One major sub-domain in the subject of polling public opinion with social media data is electoral prediction. Electoral prediction utilizing social media data potentially would significantly affect campaign strategies, c…
PredictionTime SeriesTime Series AnalysisPredicting Human Depression with Hybrid Data Acquisition utilizing Physical Activity Sensing and Social Media Feeds
Mental disorders including depression, anxiety, and other neurological disorders pose a significant global challenge, particularly among individuals exhibiting social avoidance tendencies. This study proposes a hybrid ap…
Activity RecognitionSentiment AnalysisHashing it Out: Predicting Unhealthy Conversations on Twitter
Personal attacks in the context of social media conversations often lead to fast-paced derailment, leading to even more harmful exchanges being made. State-of-the-art systems for the detection of such conversational dera…
Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities
The ability to obtain accurate food security metrics in developing areas where relevant data can be sparse is critically important for policy makers tasked with implementing food aid programs. As a result, a great deal o…
Crop Yield PredictionSentiment AnalysisSentiment ClassificationTransfer Learning