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In the wake of super typhoon Yolanda (known internationally as Haiyan) in the Philippines in 2013, many individuals in the Philippines turned to social media to express their thoughts and emotions in a variety of languages. In order to understand and analyze the sentiment of populations on the ground, we used a novel approach of developing a conceptual Linguistic Inquiry and Word Count (LIWC) dictionary comprised of Tagalog words relating to disaster. This work-in-progress paper documents our process of filtering and choosing terms and offers suggestions for validating the dictionary. When results on how the dictionary was used are available, we can better assess the process for creating conceptual LIWC dictionaries.

Increased interaction, adaptability, diversity, and emergence are all hallmarks of complexity (Miller and Page 2007; see Simon 1996). While anthropologists may not use these specific complexity theory terms, they have long been interested in how diverse people interact and adapt in their negotiation of identity and society and what sorts of social phenomena emerge from these interactions. A complexity theory perspective can interpret culture or cultural practices as either the base rules from which identity emerges (consider Appadurai 1996) or the emergent system itself, the "webs of significance" in which humans are embedded (see Geertz 1973).

BackgroundIt is challenging to assess the quality of care and detect elder abuse in nursing homes, since patients may be incapable of reporting quality issues or abuse themselves, and resources for sending inspectors are limited.ObjectiveThis study correlates Google reviews of nursing homes with Centers for Medicare and Medicaid Services (CMS) inspection results in the Nursing Home Compare (NHC) data set, to quantify the extent to which the reviews reflect the quality of care and the presence of elder abuse.MethodsA total of 16,160 reviews were collected, spanning 7,170 nursing homes. Two approaches were tested: using the average rating as an overall estimate of the quality of care at a nursing home, and using the average scores from a maximum entropy classifier trained to recognize indications of elder abuse.ResultsThe classifier achieved an F-measure of 0.81, with precision 0.74 and recall 0.89. The correlation for the classifier is weak but statistically significant: = 0.13, P < .001, and 95% confidence interval (0.10, 0.16). The correlation for the ratings exhibits a slightly higher correlation: = 0.15, P < .001. Both the classifier and rating correlations approach approximately 0.65 when the effective average number of reviews per provider is increased by aggregating similar providers.ConclusionsThese results indicate that an analysis of Google reviews of nursing homes can be used to detect indications of elder abuse with high precision and to assess the quality of care, but only when a sufficient number of reviews are available.

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