After the impressive diffusion of social media and microblogging websites
of the last years, the identification of users having the capability of influencing other
users’ choices has becoming an important research topic because of the opportunities
it can offer to many business companies. We proposed to model the
contents of tweets posted by users to express opinions on items with a three-layer
network. Layers represent users, items, and keywords, along with intra-layer interactions
among the actors of the same layer. Inter-layer connections are triples (u,i,k)
expressing the information that a user u comments on an item i by using a keyword
k. By exploiting multilinear algebra, we present a method capable to extract the most
active users stating their point of view about dominant items tagged with dominant
keywords. Experiments on two real use cases show the ability of the approach to find
influential users very active in posting opinions about the topic of interest.
If you use this dataset, please cite the following papers:
A Methodology for Identifying Influencers and their Products Perception on Twitter.
Detecting Topic Authoritative Social Media Users: a Multilayer Network Approach.
IEEE Transactions on Multimedia 2017
We applied SocialAU to a real
world dataset regarding the tweets posted by people about smartphones. The study
stems from the requirement of mobile phone manufacturers to enhance their smartphone
branding and competitive positioning in the international market. E-commerce
websites apply marketing techniques to recommend their products to customers. So,
they need to better understand social network key influencers, brand perception, users
preferences by addressing key questions like:
(a) Who is talking about smartphones?
(b) Who are the top influencers and what characterize them?
(c) Which smartphone models are generating most interest?
(d) What aspects or opinion are people associating with every phone brands?
Edge of tensor: 26673
Dataset TV Series:
TV Series: 12
Edge of tensor: 51534
Edge of tensor: from 10K to 100K by step 10K