Filtering Instagram hashtags through Crowd Tagging and the HITS algorithm

Description

Instagram is a rich source for mining descriptive tags for images and multimedia in general. The tags-image pairs can be used to train automatic image annotation (AIA) systems in accordance with the learning by example paradigm. In previous studies we had concluded that, on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stop-hashtags, that are used across totally different images just for gathering clicks and for search ability enhancement. In this work, we present a novel methodology, based on the principles of collective intelligence, that helps locating those hashtags. In particular, we show that the application of a modified version of the well known HITS algorithm, in a crowd tagging context, provides an effective and consistent way for finding pairs of Instagram images and hashtags, that lead to representative and noise-free training sets for content based image retrieval. As a proof of concept we used the crowdsourcing platform Figure-eight to allow collective intelligence to be gathered in the form of tag selection (crowdtagging) for Instagram hashtags. The crowd tagging data of Figure-eight are used to form bipartite graphs in which the fifirst type of nodes corresponds to the annotators and the second type to the hashtags they selected. The HITS algorithm is fifirst used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hashtags per image.