Retweet Brand

The behaviour of retweets and Twitter data is very valuable to marketers and their brands.

Times of India reports that a new study has revealed the best time for getting you more retweets. Researchers at the University of Maryland have demonstrated that an algorithm that takes into account the past activity of each of your followers and makes predictions about future tweeting can lead to more retweets than other commonly used methods, such as posting at peak traffic times. The Internet is full of advice about when to tweet to gain maximum exposure, but the new study subjects marketing folk wisdom to scientific scrutiny. Researcher William Rand, along with co-authors, examined the retweeting patterns of 15,000 Twitter followers during two separate five-week intervals in 2011 and 2012, from 6 am to 10 pm.

Retweets are especially valuable to marketers because they help to spread a brand's message beyond core followers. Most marketers are well aware there's a pattern to Twitter traffic. In the early morning, nothing much happens. Then people get into work and retweet intensely, as they do their morning surfing. The number of retweets drops as the day progresses, with a slight uptick at 5 pm. Then it picks up again later "when people get back to their computers after dinner, or are out at a bar or restaurant using their phones," as Rand puts it. Monday through Friday follow roughly that pattern, but Saturday and Sunday show markedly different behaviour, with much smaller morning spikes and fewer declines during the day. A "seasonal" model of posting -- the folk-wisdom model -- would suggest posting whenever there are peaks in that recurring weekly pattern.

The algorithm that the authors wrote, which took each individual's behaviour into account, was the most successful at generating retweets. The paper serves as a demonstration that applying analytic methods to Twitter data can improve a brand's ability to spread its message. The study appears in Proceedings of Advances in Social Networks Analysis and Mining.

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