Measurable Success
Success can now be collected and measured as numerical representations of online activity, in real time and updated constantly. TV shows can see how well the audience is responding to content unlimited by the dimensions of time and space. Success and popularity can be measured and compared by the count of interactivity from users. It can affect careers of politicians and celebrities, the lifespan of shows, can make or break technologies and companies, and is having a massive effect on how the world is being shaped. This blog post is going to look at a few examples of how the collection and results of big data has affected these entities.
The popularity, particularly among younger demographics, of politicians and celebrities; films and TV shows, can be categorized and measured by the amount of interaction and following they have online. Politicians with a higher number of followers or likes appear more popular than their counterparts who generate less measurable data. Celebrities and their level of fame are relevant to their number of online supporters. Films and television shows are followed, re-tweeted, shared, and liked into popularity; low interactivity levels threatening to affect their lifespan and capital growth, both economic and symbolic (see more about types of capital in week 8 blog post).
The ability to collect and collate big data enables the popularity of major figures to be easily displayed, interpreted, and analyzed (Siegel, 2013). This data can inform and affect political campaigns, pop culture, and the mainstream media (to name a few). For example, the data received from twitter informs news programs on the popularity and level of engagement audience members have to its specific segments. The show, for example The Project, is able to collect data and analyze the content to form decisions about its:
- Presenters
- Segments
- Content
- Audience engagement in respect to the above aspects of the program
By analyzing the number of Tweets received by the show during the program, executives can make decisions about the program and how to better engage the audience (Woodford, Proud, & Bruns; 2014). This could be by stopping a segment, or replacing a presenter.
New media conglomerates like Facebook and Twitter have technologies ingrained in their structure enabling mass data to be collected and displayed to the companies themselves, and users. What was previously reserved for conglomerates able to hire audience researchers, or extrapolate information from the ratings, is now accessible by almost all online entities. This process enables even the smallest of blogs to gather data about it’s reception and put it to use; possibly tailoring itself even more so to its primary audience’s wants. The ability to collect mass data has transformed media and communications research. The ratings and large-scale research companies are no longer the only bodies able to provide audience data.
Bibliography
Siegel, Eric, (2013). Introduction : The Prediction Effect. In Siegel, Eric, Predictive analytics : the power to predict who will click, buy, lie, or die, (pp.1 - 16). Hoboken, NJ: Wiley.













