%0 Conference Paper %1 thangarajan_analyzing_2015 %A Thangarajan, Narendran %A Green, Nella %A Gupta, Amarnath %A Little, Susan %A Weibel, Nadir %B Proceedings of the Conference on Wireless Health %C New York, NY, USA %D 2015 %I ACM %K HIV, Twitter, analysis, data digital epidemiology, graph modeling, networks, prevention, social visualizations %P 11:1--11:8 %R 10.1145/2811780.2811923 %T Analyzing Social Media to Characterize Local HIV At-risk Populations %U http://doi.acm.org/10.1145/2811780.2811923 %X The number of new HIV infections per year in the U.S. has remained stable at 50,000 since the 1990's. To improve epidemic control, we need more public health tools that are aimed at decreasing HIV transmission. Online social networks and their real-time communication capabilities are emerging as novel platforms for conducting epidemiological studies and recent research has outlined the feasibility of using Twitter to study HIV epidemiology. We propose a new method for identifying HIV at-risk populations using publicly available data from Twitter as an indicator of HIV risk. In this paper we take existing approaches further by introducing a new infrastructure to collect, classify, query and visualize these data, and we show the feasibility of identifying and characterizing HIV at-risk populations in the San Diego area at a finer level of granularity. %@ 978-1-4503-3851-6

@inproceedings{thangarajan_analyzing_2015, abstract = {The number of new HIV infections per year in the U.S. has remained stable at 50,000 since the 1990's. To improve epidemic control, we need more public health tools that are aimed at decreasing HIV transmission. Online social networks and their real-time communication capabilities are emerging as novel platforms for conducting epidemiological studies and recent research has outlined the feasibility of using Twitter to study HIV epidemiology. We propose a new method for identifying HIV at-risk populations using publicly available data from Twitter as an indicator of HIV risk. In this paper we take existing approaches further by introducing a new infrastructure to collect, classify, query and visualize these data, and we show the feasibility of identifying and characterizing HIV at-risk populations in the San Diego area at a finer level of granularity.}, added-at = {2019-02-18T07:11:07.000+0100}, address = {New York, NY, USA}, author = {Thangarajan, Narendran and Green, Nella and Gupta, Amarnath and Little, Susan and Weibel, Nadir}, biburl = {https://www.bibsonomy.org/bibtex/23890eaf28f4831444d4d08e38726c14a/weibel}, booktitle = {Proceedings of the {Conference} on {Wireless} {Health}}, doi = {10.1145/2811780.2811923}, file = {ACM Full Text PDF:/Users/weibel/Zotero/storage/29U298AY/Thangarajan et al. - 2015 - Analyzing Social Media to Characterize Local HIV A.pdf:application/pdf}, interhash = {49f82be02b37218ba544b332fb29cba3}, intrahash = {3890eaf28f4831444d4d08e38726c14a}, isbn = {978-1-4503-3851-6}, keywords = {HIV, Twitter, analysis, data digital epidemiology, graph modeling, networks, prevention, social visualizations}, pages = {11:1--11:8}, publisher = {ACM}, series = {{WH} '15}, timestamp = {2019-02-18T07:11:07.000+0100}, title = {Analyzing {Social} {Media} to {Characterize} {Local} {HIV} {At}-risk {Populations}}, url = {http://doi.acm.org/10.1145/2811780.2811923}, urldate = {2018-12-06}, year = 2015 }