Businesses communicate using Twitter for a variety of reasons -- to raise awareness of their brands, to market new products, to respond to community comments, and to connect with their customers and potential customers in a targeted manner. For businesses to do this effectively, they need to understand which content and structural elements about a tweet make it influential, that is, widely liked, followed, and retweeted. This paper presents a systematic methodology for analyzing commercial tweets, and predicting the influence on their readers. Our model, which use a combination of decoration and meta features, outperforms the prediction ability of the baseline model as well as the tweet embedding model. Further, in order to demonstrate a practical use of this work, we show how an unsuccessful tweet may be engineered (for example, reworded) to increase its potential for success.
Towards Successful Social Media Advertising: Predicting the Influence of Commercial Tweets
Renhao Cui,G. Agrawal,Rajiv Ramnath
Published 2019 in arXiv.org
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- Publication year
2019
- Venue
arXiv.org
- Publication date
2019-10-28
- Fields of study
Business, Computer Science
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Semantic Scholar
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