An Algorithm for Constructing Associative Series of Hashtags for Semantic Navigation in Social Networks

Nowadays hashtags are an important mechanism of semantic navigation in social media. In this study, we consider the solution of the problem of building associative series of hashtags for one of the largest social networks. These series should meet two criteria: they should be short and shouldn’t have wide semantic gaps between sequential hashtags. An algorithm that allows us to create an associative series of hashtags could be used to increase the quantity of hashtags in posts, which will facilitate semantic navigation through posts in a social network. The paper proposes a formal definition of the semantic path building problem as a multicriteria optimization problem on the co-occurrence network of hashtags in posts. First, we built a co-occurrence network for hashtags from a big dataset of messages from Instagram. Then, we develop a combined optimization function for both criteria from the semantic path building problem. For measuring semantic similarity between hashtags, we use a metric based on the word2vec embeddings of hashtags. Using empirical paths obtained with various algorithms, we tune the parameters of a generalized optimization function that can be used to construct semantic paths using Dijkstra’s pathfinding or special greedy algorithms.

social network, hashtag, hashtags recommendation, pathfinding, semantic navigation