Social Network Advertising Classification Based on Content Categories

Social media usage is expanding in different sectors of society. As a consequence, a large amount of User-Generated-Content is produced every day. Due to its different effects on users, content management is essential for business advertising on these platforms. However, in view of social media’s large volume of content, measuring the effect on users entails high costs and effort. This paper examines the use of machine learning techniques to reduce the cost and effort of this kind of analysis. To this end, an automatic document classification is employed to check its viability by testing it in the companies publications in Facebook. The results show that the machine learning classifier obtained has an excellent potential to measure effectiveness and analyze a significant amount of content with more efficiency. The classifier has practical implications since it allows an extensive competitor analysis to be conducted and is also able to influence social media campaigns.

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