This paper discusses the opportunities and challenges present for the next generation analyst in the use of social media data. Focusing particularly on the detection of deception and misinformation within the latter, a review of current approaches is followed by the elaboration of a theoretical model for social media analysis premised on activity based intelligence. Considering this model with regard to latent challenges to analytical performance and potential opportunities for analytical calibration, this discussion articulates an approach for open-source, next generation intelligence analysis.
This is the first academic paper which focuses specifically on the new social media application Yik Yak. To provide a
solid foundation, a brief overview of a few anonymous social media platforms is provided. A social media sensor
framework is then presented which utilizes a three-layered approach to addressing the use of analytic tools. Specifically
the use of keyword, geolocation, sentiment, and network analysis is explored through the perspective of social media as
a sensor. Challenges and criticisms are exposed in addition to some possible solutions. A theoretical case study is then
offered which outlines a potential use of social media as a senor for emergency managers. The paper culminates with a
data collection for the development of a lexicon for Yik Yak. This data collection focuses on an 18 day study which
collects Yik Yak posts and Twitter tweets simultaneously. The top 100 keywords for each platform are collected for
every 24 hour period and placed through a relative change comparison. Overall, Yik Yak offers a more stable baseline as
compared to Twitter.