Abstract
To improve a tweet in Twitter, we would like to estimate the effectiveness of a draft before it is sent. The total number of retweets of a tweet can be considered as a measure for the tweet’s effectiveness. To estimate the number of retweets for an author, we propose a procedure to learn a personalized model from his/her past tweets. We propose three types of new features based on the contents of the tweets: Entity, Pair, and Topic. Empirical results from seven authors indicate that the Pair and Topic features have statistically significant improvements on the correlation coefficient between the estimates and the actual numbers of retweets. We study different combinations of the three types of features, and many of the combinations significantly improve the result further.
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Notes
We define the Domain Stop Words as the words belong to the web site instead of the article. For all pages from the same web site (domain), the words in the menu and even in the advertisement are usually the same. For this reason, we generate an independent list of stop words for each domain. A stop word in a domain is the one that appears in more than 80% of pages which we crawled. When we use a web page to extract features, we remove the words listed in the Domain Stop Words.
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Sun, X., Chan, P.K. Estimating effectiveness of twitter messages with a personalized machine learning approach. Knowl Inf Syst 56, 27–53 (2018). https://doi.org/10.1007/s10115-017-1088-3
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DOI: https://doi.org/10.1007/s10115-017-1088-3