Hashtags Influence Maximization: Choosing the most Influential Hashtags on Instagram
Keywords:Influence maximization, information diffusion, data analysis, social network, Instagram
This study aims to find influential hashtags using Influence Maximization (IM). The IM approach was implemented using hashtags network collected from Instagram. This study can help business or ordinary users to choose the most engaging hashtags for posting, as opposed to selecting influencers, which was widely studied using the IM approach. The network was build based on the hashtags co-appearance frequency. Three IM algorithms, i.e. SSA, DSSA, and IMM, were simulated under the IC and LT models. The algorithms were compared against TopUsage, which is the top hashtags based on the usage count. The IM algorithms have a similar performance with TopUsage in influence spread, which shows that IM can adapt to the hashtags network. However, the IM algorithms produced better hashtags based on the UER (unique engagement rate) metric. The best UER performance was shown by DSSA under the LT model, where it outperformed TopUsage by 17.23%. In the hashtags categorization scenario, DSSA-LT outperformed the UER of TopUsage by up to 6.87%. This categorization is more useful in a practical scenario, to find only relevant hashtags for posting. The hashtags generated by DSSA-LT are about 30-35% different from TopUsage.
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