In this paper, we demonstrate how the state-of-the-art machine learning and text mining techniques can be used to build effective social media-based substance use detection systems. Since a substance use ground truth is difficult to obtain on a large scale, to maximize system performance, we explore different feature learning methods to take advantage of a large amount of unsupervised social media data. We also demonstrate the benefit of using multi-view unsupervised feature learning to combine heterogeneous user information such as Facebook "likes" and "status updates" to enhance system performance. Based on our evaluation, our best models achieved 86 AUC for predicting tobacco use, 81 for alcohol use and 84 for drug use, all of which significantly outperformed existing methods. Our investigation has also uncovered interesting relations between a user's social media behavior (e.g., word usage) and substance use.
Substance use disorder (SUD) encompasses a complex pattern of behaviors. Many studies have been conducted to discover factors interacting with SUD. A growing number of studies have confirmed a strong association between personal traits and substance use. For example, @cite_38 found that smokers have significantly higher openness to experience and lower conscientiousness , a personality trait related to a tendency to show self-discipline, act dutifully, and aim for achievement. @cite_30 examined the links between alcohol consumption and personality and found that alcohol use is correlated positively with sociability and extraversion. @cite_17 conducted a study involving 1102 participants and found a link between drug use and low conscientiousness . @cite_22 revealed risk factors related to addiction such as age, sex, impulsivity, sweet-liking, novelty reactivity, proclivity for exercise, and environmental impoverishment. Additionally, addiction is also linked to environmental and social factors such as neighborhood environment @cite_31 , family environment @cite_14 @cite_21 and social norms @cite_12 @cite_29 .
@cite_30 @cite_38 @cite_14 @cite_22 @cite_29 @cite_21 @cite_31 @cite_12 @cite_17
2072878461 2095048077 2338708958 2057979354 1206398332
Abstract The present study examines the link between alcohol consumption and personality, using the California Psychological Inventory in a large well-adjusted sample and tests four hypotheses, including one that states that persons who drink no alcohol at all are less well adjusted than persons who drink moderate amounts. The results confirm the hypotheses. Alcohol consumed correlates positively with sociability and extraversion, but negatively with conscientiousness and willingness to conform. The results also show, in a large sample and using a comprehensive personality inventory, that persons who drink no alcohol at all are a little more withdrawn, a little less ambitious, a little less generally well-organised and competent, than people who drink in moderation. Historically, papers have been physically bound to the journal in which they were published; but in the digital age papers are available individually, no longer tied to their respective journals. Hence, papers now can be read and cited based on their own merits, independently of the journal's physical availability, reputation, or impact factor (IF). We compare the strength of the relationship between journals' IFs and the actual citations received by their respective papers from 1902 to 2009. Throughout most of the 20th century, papers' citation rates were increasingly linked to their respective journals' IFs. However, since 1990, the advent of the digital age, the relation between IFs and paper citations has been weakening. This began first in physics, a field that was quick to make the transition into the electronic domain. Furthermore, since 1990 the overall proportion of highly cited papers coming from highly cited journals has been decreasing and, of these highly cited papers, the proportion not coming from highly cited journals has been increasing. Should this pattern continue, it might bring an end to the use of the IF as a way to evaluate the quality of journals, papers, and researchers. © 2012 Wiley Periodicals, Inc. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles.