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Title: Identifying image tags from Instagram hashtags using the HITS algorithm
Authors: Giannoulakis, Stamatios 
Tsapatsoulis, Nicolas 
Ntalianis, Klimis S. 
Keywords: Bipartite network;Graphs;Hashtags;HITS algorithm;Image retrieval;Image tagging;Instagram;Machine learning
Category: Computer and Information Sciences
Field: Natural Sciences
Issue Date: 29-Mar-2018
Publisher: IEEE
Source: 15th IEEE International Conference on Dependable, Autonomic and Secure Computing 15th IEEE International Conference on Pervasive Intelligence and Computing 3rd IEEE International Conference on Big Data Intelligence and Computing 2017 IEEE Cyber Science and Technology Congress, 2017, Orlando, Florida, 6-10 November
Project: Network for sOcial compuTing REsearch (NOTRE) 
Abstract: Many automatic image annotation methods are based on the learning by example paradigm. Image tagging, through manual image inspection, is the first step towards this end. However, manual image annotation, even for creating the training sets, is time-consuming, complicated and contains human subjectivity errors. Thus, alternative ways for automatically creating training examples, i.e., pairs of images and tags, are crucial. As we showed in one of our previous studies, tags accompanying photos in social media and especially the Instagram hashtags can be used for image annotation. However, it turned out that only a 20% of the Instagram hashtags are actually relevant to the content of the image they accompany. Identifying those hashtags through crowdsourcing is a plausible solution. In this work, we investigate the effectiveness of the HITS algorithm for identifying the right tags in a crowdsourced image tagging scenario. For this purpose, we create a bipartite graph in which the first type of nodes corresponds to the annotators and the second type to the tags they select, among the hashtags, to annotate a particular Instagram image. From the results, we conclude that the authority value of the HITS algorithm provides an accurate estimation of the appropriateness of each Instagram hashtag to be used as a tag for the image it accompanies while the hub value can be used to filter out the dishonest annotators.
DOI: 10.1109/DASC-PICom-DataCom-CyberSciTec.2017.29
Rights: © 2017 IEEE
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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