On SAGE Insight: The “shape” of interaction networks on Twitter

From International Journal of Market Research

This paper summarizes learnings gleaned from mapping numerous brand, category, and campaign interaction networks on Twitter. It characterizes the different network “shapes,” or typologies, that emerge from discussions about brands, campaigns, and categories and places these shapes on a continuum from highly centralized to highly decentralized. The article discusses how brands might strategically and tactically use these insights to maximize their communications and brand building. “Digital” sources such as social media give us an unprecedented view into the lives of customers, and all of these data arrive simply through passive data collection. We can now measure an entire population (such as everyone who interacted with a digital campaign) directly to gain results with minimal inference. This means that market research needs to adopt and develop new tools, many of which are informed by software development practices and data science.

This article summarizes learnings gleaned from mapping numerous brand, category, and campaign interaction networks on Twitter. It characterizes the different network “shapes,” or typologies, that emerge from discussions about brands, campaigns, and categories and places these shapes on a continuum from highly centralized to highly decentralized. The article discusses how brands might strategically and tactically use these insights to maximize their communications and brand building. “Digital” sources such as social media give us an unprecedented view into the lives of customers, and all of these data arrive simply through passive data collection. We can now measure an entire population (such as everyone who interacted with a digital campaign) directly to gain results with minimal inference. This means that market research needs to adopt and develop new tools, many of which are informed by software development practices and data science.

This study is predominantly focused on social conversations around brands, campaigns, and product categories on Twitter. The general methods and paradigms that are described have broad applicability across social platforms, especially when coupled with the power of text and image mining. It highlights the need for market researchers to help their client brands to think more strategically about the social ecosystem that they find themselves embedded in.

Abstract

This article is concerned with qualitative descriptions of the morphological structure of Twitter interaction network topologies. It summarizes learnings gleaned from mapping numerous brand, category, and campaign interaction networks on Twitter. The article characterizes the different network “shapes,” or typologies, that emerge from discussions about brands, campaigns, and categories and places these shapes on a continuum from highly centralized to highly decentralized. The article discusses how brands might strategically and tactically use these insights to maximize their communications and brand building. This article is a substantially altered version of an article that was originally presented at the ESOMAR Congress conference in Dublin, Ireland, in September 2015.

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Article details
The “shape” of interaction networks on Twitter
Kyle Findlay
First Published September 17, 2018 Research Article
DOI: 10.1177/1470785318765695
International Journal of Market Research

 

 

     
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