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Creators: Khan Muhammad Saqiful Alam
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Subject: Social Media, Regulations
DOI: doi:10.25540/6ST2-5C3N

Today, the world of social media encompasses at least half of the habitants of earth, and is larger than any country or continent. As such, the social media companies need to operate across national boundaries, cultures, languages, laws and regulations. As a result of this, it can be expected that there would be no fit-for-all strategy of how a social media company operates in varying socio-geo-political conditions, and they would need to localise their business practices to fit the country/region wise idiosyncrasies. But so far there has been no comprehensive studies that highlight the factors that determine the optimum strategy by a social media company to tackle regulators. One challenge to do such a research is the lack of internal and strategic data of how a social media company considers the prospects and risks of a location. This paper is able to use such internal data about the strategies used by a social media company, and the assessment of risk of geographic locations and the user behavior and risks that the social media platform is exposed to. Using the transaction cost approach, the paper argues that the optimum strategy of the company to tackle the regulators depends on the extent of problematic content, the level of riskiness in the market, and whether certain forms of problematic content are present in the platform. This research can be a guideline for social media companies around the world on how they can work hand-in-hand with regulators and policymakers in different parts of the world, and also gives the policymakers a level of understanding of what factors causes a social media company to invest in having a more cooperative approach towards regulators.

Citation: Khan Muhammad Saqiful Alam (2021-12-17). HOW A SOCIAL MEDIA NAVIGATES THROUGH REGULATORY WATERS – IDENTIFYING THE KEY FACTORS. ScholarBank@NUS Repository. [Dataset].
License: Attribution-NonCommercial 4.0 International
Appears in Collections:Staff Dataset

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