Abstract
This study investigates social media trends and proposes a buzz tweet classification method to explore the factors causing the buzz phenomenon on Twitter. It is difficult to identify the causes of the buzz phenomenon based solely on texts posted on Twitter. It is expected that by limiting the tweets to those with attached images and using the characteristics of the images and the relationships between the text and images, a more detailed analysis than that of with text-only tweets can be conducted. Therefore, an analysis method was devised based on a multi-task neural network that uses both the features extracted from the image and text as input and the buzz class (buzz/non-buzz) and the number of “likes (favorites)” and “retweets (RTs)” as output. The predictions made using a single feature of the text and image were compared with the predictions using a combination of multiple features. The differences between buzz and non-buzz features were analyzed based on the cosine similarity between the text and the image. The buzz class was correctly identified with a correctness rate of approximately 80% for all combinations of image and text features, with the combination of BERT and VGG16 providing the highest correctness rate.
Highlights
With the development of social networking services (SNSs), information can be shared and spread in real time among many users
ResTualbtsle 4 shows the results of the predicted correct response rate of the buzz class for eTaacbhleim4 asgheowfesathuereremsuoldtseloof bthtaeinperdediincttehdeceoxrpreecrtimreesnpto,nTsaebrleat5e oshf othwesbtuhzezrcelsauslstsfoorf eparcehdiimctaiogne ufesaintugroenmlyodteexltofbetaatiunreeds,ianntdheTeaxbplee6rimsheonwt,sTtahbelere5sushltoswofspthredriecstuioltnsuosfipnrgeodnicl-y timonaugesifnegatounrleys.teTxatbfleea7tusrheosw, asntdheTarebsleul6tsshoof wprsetdhiectrioesnuultssinogf psrinegdliecttiaosnkulesianrgnionnglymiomdaegl.e features
The model with the highest correct prediction rate for image features alone was VGG16 at 0.75. With both text and image features used as the input, the correct prediction rate of VGG16 for the image features was 0.84, which was higher than that of the other features
Summary
With the development of social networking services (SNSs), information can be shared and spread in real time among many users. There are many examples of successful buzz marketing, including Ezaki Glico’s “Pocky Day” event on 11 November, Softbank’s “Free Mobile Phone Bills for Life Campaign”, and Seven-Eleven’s “Beard Straws” among others. These campaigns have increased the number of people accessing their websites by utilizing the diffusion power of SNSs. These campaigns have increased the number of people accessing their websites by utilizing the diffusion power of SNSs For marketing purposes, it would be useful if such trends on the web, triggered by SNS content, could be quickly detected
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