Time-aware adaptive tweets ranking through deep learning

Generally, tweets about brands, news and so forth, are mostly delivered to the Twitter user in a reverse chronological order choosing among those twitted by the so-called followed users. Recently, Twitter is facing with information overload by introducing new filtering features, such as “while you are away” in order to show only a few tweets summarizing the posted ones, and ranking the tweets considering the quality, in addition to timeliness. Trivially enough we state that the strategy to rank the tweets to maximize the user engagement and, why not, augmenting the tweet and re-tweet rates, is not unique. There are several dimensions affecting the ranking, such as time, location, semantic, publisher authority, quality, and so on. We point out that the tweet ranking model should vary according to the user's context, interests and how those change along the timeline, cyclically, weekly or at specific date-time when the user logs in. In this work, we introduce a deep learning method attempting to re-adapt the ranking of the tweets by preferring those that are more likely interesting for the user. User's interests are extracted by mainly considering previous user re-tweets, replies and also the time when they occurred. We evaluate a ranking model by measuring how many tweets that will be re-tweeted in the near future were included in the top-ranked tweet list. The results of the proposed ranking model revealed good performances overcoming the methods that consider only the reverse-chronological order or user's interest score. In addition, we pointed out that in our dataset the most impacting features on the performance of proposed ranking model are: publisher authority, tweet content measures, and time-awareness.