Predicting iPhone Sales from iPhone Tweets

Recent research in the field of computational social science have shown how data resulting from the widespread adoption and use of social media channels such as twitter can be used to predict outcomes such as movie revenues, election winners, localized moods, and epidemic outbreaks. Underlying assumptions for this research stream on predictive analytics are that social media actions such as tweeting, liking, commenting and rating are proxies for user/consumer’s attention to a particular object/product and that the shared digital artefact that is persistent can create social influence. In this paper, we demonstrate how social media data from twitter can be used to predict the sales of iPhones. Based on a conceptual model of social data consisting of social graph (actors, actions, activities, and artefacts) and social text (topics, keywords, pronouns, and sentiments), we develop and evaluate a linear regression model that transforms iPhone tweets into a prediction of the quarterly iPhone sales with an average error close to the established prediction models from investment banks. This strong correlation between iPhone tweets and iPhone sales becomes marginally stronger after incorporating sentiments of tweets. We discuss the findings and conclude with implications for predictive analytics with big social data

Predictive Analytics with Big Social Data

Recent research in the field of computational social science have shown how data resulting from the widespread adoption and use of social media channels such as twitter can be used to predict outcomes such as movie revenues, election winners, localized moods, and epidemic outbreaks. Underlying assumptions for this research stream on predictive analytics are that social media actions such as tweeting, liking, commenting and rating are proxies for user/consumer’s attention to a particular object/product and that the shared digital artefact that is persistent can create social influence. In this paper, we demonstrate how social media data from twitter and facebook can be used to predict the quarterly sales of iPhones and revenues of H&M respectively. Based on a conceptual model of social data consisting of social graph (actors, actions, activities, and artefacts) and social text (topics, keywords, pronouns, and sentiments), we develop and evaluate linear regression models that transform (a) iPhone tweets into a prediction of the quarterly iPhone sales with an average error close to the established prediction models from investment banks (Lassen, Madsen, & Vatrapu, 2014)and (b) facebook likes into a prediction of the global revenue of the fast fashion company, H&M. We discuss the findings and conclude with implications for predictive analytics with big social data

Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology

Microbiota-host-diet interactions contribute to the development of metabolic diseases. Imidazole propionate is a novel microbially produced metabolite from histidine, which impairs glucose metabolism. Here, we show that subjects with prediabetes and diabetes in the MetaCardis cohort from three European countries have elevated serum imidazole propionate levels. Furthermore, imidazole propionate levels were increased in subjects with low bacterial gene richness and Bacteroides 2 enterotype, which have previously been associated with obesity. The Bacteroides 2 enterotype was also associated with increased abundance of the genes involved in imidazole propionate biosynthesis from dietary histidine. Since patients and controls did not differ in their histidine dietary intake, the elevated levels of imidazole propionate in type 2 diabetes likely reflects altered microbial metabolism of histidine, rather than histidine intake per se. Thus the microbiota may contribute to type 2 diabetes by generating imidazole propionate that can modulate host inflammation and metabolism

Chronic Citalopram Administration Causes a Sustained Suppression of Serotonin Synthesis in the Mouse Forebrain

BACKGROUND:Serotonin (5-HT) is a neurotransmitter with important roles in the regulation of neurobehavioral processes, particularly those regulating affect in humans. Drugs that potentiate serotonergic neurotransmission by selectively inhibiting the reuptake of serotonin (SSRIs) are widely used for the treatment of psychiatric disorders. Although the regulation of serotonin synthesis may be an factor in SSRI efficacy, the effect of chronic SSRI administration on 5-HT synthesis is not well understood. Here, we describe effects of chronic administration of the SSRI citalopram (CIT) on 5-HT synthesis and content in the mouse forebrain. METHODOLOGY/PRINCIPAL FINDINGS:Citalopram was administered continuously to adult male C57BL/6J mice via osmotic minipump for 2 days, 14 days or 28 days. Plasma citalopram levels were found to be within the clinical range. 5-HT synthesis was assessed using the decarboxylase inhibition method. Citalopram administration caused a suppression of 5-HT synthesis at all time points. CIT treatment also caused a reduction in forebrain 5-HIAA content. Following chronic CIT treatment, forebrain 5-HT stores were more sensitive to the depleting effects of acute decarboxylase inhibition. CONCLUSIONS/SIGNIFICANCE:Taken together, these results demonstrate that chronic citalopram administration causes a sustained suppression of serotonin synthesis in the mouse forebrain. Furthermore, our results indicate that chronic 5-HT reuptake inhibition renders 5-HT brain stores more sensitive to alterations in serotonin synthesis. These results suggest that the regulation of 5-HT synthesis warrants consideration in efforts to develop novel antidepressant strategies