Predicting Public Opinion on Drug Legalization: Social Media Analysis and Consumption Trends
暂无分享,去创建一个
Amit P. Sheth | Krishnaprasad Thirunarayan | Michael L. Raymer | Saeedeh Shekarpour | Farahnaz Golrooy Motlagh | A. Sheth | K. Thirunarayan | M. Raymer | Saeedeh Shekarpour | F. Motlagh
[1] Amit P. Sheth,et al. Twitris: A System for Collective Social Intelligence , 2014, Encyclopedia of Social Network Analysis and Mining.
[2] Mercedes Torres Torres,et al. Emotion and polarity prediction from Twitter , 2017, 2017 Computing Conference.
[3] Eva Petkova,et al. Correlates of intentions to use cannabis among US high school seniors in the case of cannabis legalization. , 2014, The International journal on drug policy.
[4] S. Watson,et al. Marijuana and medicine: assessing the science base: a summary of the 1999 Institute of Medicine report. , 2000, Archives of general psychiatry.
[5] Hongbo Xu,et al. Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis , 2009, ECIR.
[6] Tianshu Feng,et al. How does state marijuana policy affect US youth? Medical marijuana laws, marijuana use and perceived harmfulness: 1991-2014. , 2016, Addiction.
[7] Michael D. Barnes,et al. Tracking suicide risk factors through Twitter in the US. , 2014, Crisis.
[8] Justin B. Moore. On Avoiding an Abstraction of the Abstract: Preparing Abstracts for Submissions to the Journal of Public Health Management and Practice , 2009 .
[9] Melissa J. Krauss,et al. Twitter chatter about marijuana. , 2015, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.
[10] Amit P. Sheth,et al. Implicit Entity Recognition in Clinical Documents , 2015, *SEMEVAL.
[11] Amit P. Sheth,et al. Extracting Diverse Sentiment Expressions with Target-Dependent Polarity from Twitter , 2012, ICWSM.
[12] Thomas Gottron,et al. Bad news travel fast: a content-based analysis of interestingness on Twitter , 2011, WebSci '11.
[13] Krishnaprasad Thirunarayan,et al. “When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets , 2016, JMIR public health and surveillance.
[14] Michael Grossman,et al. Why do Some People Want to Legalize Cannibis Use? , 2011 .
[15] Calton Pu,et al. Study of Trend-Stuffing on Twitter through Text Classification , 2010 .
[16] Z. Atakan,et al. Marijuana as Medicine? The Science beyond the Controversy , 2001, BMJ : British Medical Journal.
[17] Amelia Burke-Garcia,et al. Trending now: future directions in digital media for the public health sector. , 2014, Journal of public health.
[18] Mehmed Kantardzic,et al. Geo-Social Analytics Based on Spatio-Temporal Dynamics of Marijuana-Related Tweets , 2017, ICISDM '17.
[19] ThompsonLeah,et al. Prevalence of Marijuana-Related Traffic on Twitter, 2012–2013: A Content Analysis , 2015 .
[20] Francois R. Lamy,et al. Increases in synthetic cannabinoids-related harms: Results from a longitudinal web-based content analysis. , 2017, The International journal on drug policy.
[21] Roberto Navigli,et al. Cross level semantic similarity: an evaluation framework for universal measures of similarity , 2015, Lang. Resour. Evaluation.
[22] Janet E. Joy,et al. Marijuana as medicine? , 2002 .
[23] Frederick P. Rivara,et al. Prevalence of Marijuana-Related Traffic on Twitter, 2012-2013: A Content Analysis , 2015, Cyberpsychology Behav. Soc. Netw..
[24] Amit P. Sheth,et al. "Time for Dabs": Analyzing Twitter Data on Butane Hash Oil Use , 2015 .
[25] Brendan T. O'Connor,et al. From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series , 2010, ICWSM.
[26] Melissa J. Krauss,et al. A content analysis of tweets about high-potency marijuana. , 2016, Drug and alcohol dependence.
[27] Catherine Bartlett,et al. Twitter and public health. , 2015, Journal of public health management and practice : JPHMP.