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Digital monsters are not present in the “real” – in this case, non-digital – world in the same way as other supernatural creatures. These monsters are “born digital”; that is, they are created with digital tools within digital contexts (e.g., photo and video editors, websites), and are encountered in the genre of expressive culture now known as “Creepypasta,” transmedial, Internet-based narratives about scary, usually supernatural, things. The most famous digital monster is Slender Man, who, despite his fictional status, was cited as the inspiration for real-world acts of violence, most notably the attempted murder of a young girl in Wisconsin in 2014. Traceable to a single creator, but passing through a long, collaborative process of telling and retelling across various Internet fora and media, Slender Man embodies the emergent qualities of much Internet culture. The story of Mr. Top Hat presented here, framed as a series of blog posts by a fictional anthropologist and reader responses to them, attempts to represent the type of collaborative dynamic that produced and continually reproduces Slender Man. Like Slender Man, “Mr. Top Hat” is surrounded by ambiguity. Are aspects of his story real, despite the evidence of his fictional status? The anthropologist, Dr. Richard Morgan, starts off as a researcher of the social processes that give rise to Creepypasta, but quickly finds that there is more at stake than the creation of a story.

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Wireless Sensor Networks (WSNs) that classify the source of detected radio signals require mobile transmitters, physical (PHY) and link layer meta data, and packet sniffing capabilities. These signal classifiers are restricted by assumptions that may be difficult to realize in adversarial Signal of Opportunity (SOP) localization settings, and they do not jointly localize transmitters. In this paper, we present a novel framework that self-organizes to classify and jointly localize sets of stationary transmitters emitting SOP. The framework leverages the underlying Gaussian distribution associated with multilateration estimates via the use of Unsupervised Learning (UL) techniques. Inference of spatial multilateration features allows for the joint estimation of classification outcomes with respect to several unknown parameters, including the number of transmitters, source transmitters for each signal, the underlying multilateration distribution, and the transmitter locations. The proposed framework was evaluated in a two-dimensional trilateration experiment. Signals transmitted by vehicular Tire Pressure Monitoring System (TPMS) wireless beacons were observed by a custom-built WSN test bed to produce Received Signal Strength Indicators (RSS) features. We used a trained Convolutional Neural Network (CNN) to make location estimates from the RSS feature data. An Anderson-Darling test showed that these CNN estimates were statistically indistinguishable from those of a normal distribution. The spatial trilateration estimates were clustered to identify six of the eight TPMS transmitters with a 75% cluster detection rate, which was the result of every statistically different spatial and RSS population as determined by a Kruskal-Wallis (KW) test. The source transmitter of every signal was classified with a 76.4% indicator variable accuracy (93.7% when removing statistically identical RSS populations) and the detected source transmitters were localized with an average of 1.72 m variance and 1.19 m bias within a roughly 15 m square whose perimeter is made up of receivers.

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