GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
Material
Publisher
Person/Organisation
Language
Years
FID
Subjects(RVK)
  • 1
    Online Resource
    Online Resource
    User Perceptions of Different Electronic Cigarette Flavors on Social Media: Observational Study
    JMIR Publications Inc. ; 2020
    In:  Journal of Medical Internet Research Vol. 22, No. 6 ( 2020-6-24), p. e17280-
    Details
    In: Journal of Medical Internet Research, JMIR Publications Inc., Vol. 22, No. 6 ( 2020-6-24), p. e17280-
    Abstract: The number of electronic cigarette (e-cigarette) users has been increasing rapidly in recent years, especially among youth and young adults. More e-cigarette products have become available, including e-liquids with various brands and flavors. Various e-liquid flavors have been frequently discussed by e-cigarette users on social media. Objective This study aimed to examine the longitudinal prevalence of mentions of electronic cigarette liquid (e-liquid) flavors and user perceptions on social media. Methods We applied a data-driven approach to analyze the trends and macro-level user sentiments of different e-cigarette flavors on social media. With data collected from web-based stores, e-liquid flavors were classified into categories in a flavor hierarchy based on their ingredients. The e-cigarette–related posts were collected from social media platforms, including Reddit and Twitter, using e-cigarette–related keywords. The temporal trend of mentions of e-liquid flavor categories was compiled using Reddit data from January 2013 to April 2019. Twitter data were analyzed using a sentiment analysis from May to August 2019 to explore the opinions of e-cigarette users toward each flavor category. Results More than 1000 e-liquid flavors were classified into 7 major flavor categories. The fruit and sweets categories were the 2 most frequently discussed e-liquid flavors on Reddit, contributing to approximately 58% and 15%, respectively, of all flavor-related posts. We showed that mentions of the fruit flavor category had a steady overall upward trend compared with other flavor categories that did not show much change over time. Results from the sentiment analysis demonstrated that most e-liquid flavor categories had significant positive sentiments, except for the beverage and tobacco categories. Conclusions The most updated information about the popular e-liquid flavors mentioned on social media was investigated, which showed that the prevalence of mentions of e-liquid flavors and user perceptions on social media were different. Fruit was the most frequently discussed flavor category on social media. Our study provides valuable information for future regulation of flavored e-cigarettes.
    Type of Medium: Online Resource
    ISSN: 1438-8871
    URL: Article
    DOI: 10.2196/17280
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2020
    detail.hit.zdb_id: 2028830-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Fast Online Incremental Learning on Mixture Streaming Data
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2017
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 31, No. 1 ( 2017-02-13)
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 31, No. 1 ( 2017-02-13)
    Abstract: The explosion of streaming data poses challenges to feature learning methods including linear discriminant analysis (LDA). Many existing LDA algorithms are not efficient enough to incrementally update with samples that sequentially arrive in various manners. First, we propose a new fast batch LDA (FLDA/QR) learning algorithm that uses the cluster centers to solve a lower triangular system that is optimized by the Cholesky-factorization. To take advantage of the intrinsically incremental mechanism of the matrix, we further develop an exact incremental algorithm (IFLDA/QR). The Gram-Schmidt process with reorthogonalization in IFLDA/QR significantly saves the space and time expenses compared with the rank-one QR-updating of most existing methods. IFLDA/QR is able to handle streaming data containing 1) new labeled samples in the existing classes, 2) samples of an entirely new (novel) class, and more significantly, 3) a chunk of examples mixed with those in 1) and 2). Both theoretical analysis and numerical experiments have demonstrated much lower space and time costs (2~10 times faster) than the state of the art, with comparable classification accuracy.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v31i1.10874
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2017
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Robust online orientation correction for radiographs in PACS environments
    Institute of Electrical and Electronics Engineers (IEEE) ; 2006
    In:  IEEE Transactions on Medical Imaging Vol. 25, No. 10 ( 2006-10), p. 1370-1379
    Details
    In: IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers (IEEE), Vol. 25, No. 10 ( 2006-10), p. 1370-1379
    Type of Medium: Online Resource
    ISSN: 0278-0062
    URL: Article
    DOI: 10.1109/TMI.2006.880677
    RVK:
    XA 48667
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2006
    detail.hit.zdb_id: 2068206-2
    detail.hit.zdb_id: 622531-7
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    A Social Media Study on the Associations of Flavored Electronic Cigarettes With Health Symptoms: Observational Study
    JMIR Publications Inc. ; 2020
    In:  Journal of Medical Internet Research Vol. 22, No. 6 ( 2020-6-22), p. e17496-
    Details
    In: Journal of Medical Internet Research, JMIR Publications Inc., Vol. 22, No. 6 ( 2020-6-22), p. e17496-
    Abstract: In recent years, flavored electronic cigarettes (e-cigarettes) have become popular among teenagers and young adults. Discussions about e-cigarettes and e-cigarette use (vaping) experiences are prevalent online, making social media an ideal resource for understanding the health risks associated with e-cigarette flavors from the users’ perspective. Objective This study aimed to investigate the potential associations between electronic cigarette liquid (e-liquid) flavors and the reporting of health symptoms using social media data. Methods A dataset consisting of 2.8 million e-cigarette–related posts was collected using keyword filtering from Reddit, a social media platform, from January 2013 to April 2019. Temporal analysis for nine major health symptom categories was used to understand the trend of public concerns related to e-cigarettes. Sentiment analysis was conducted to obtain the proportions of positive and negative sentiment scores for all reported health symptom categories. Topic modeling was applied to reveal the topics related to e-cigarettes and health symptoms. Furthermore, generalized estimating equation (GEE) models were used to quantitatively measure potential associations between e-liquid flavors and the reporting of health symptoms. Results Temporal analysis showed that the Respiratory category was consistently the most discussed health symptom category among all categories related to e-cigarettes on Reddit, followed by the Throat category. Sentiment analysis showed higher proportions of positive sentiment scores for all reported health symptom categories, except for the Cancer category. Topic modeling conducted on all health-related posts showed that 17 of the top 100 topics were flavor related. GEE models showed different associations between the reporting of health symptoms and e-liquid flavor categories, for example, lower association of the Beverage flavors with Respiratory compared with other flavors and higher association of the Fruit flavors with Cardiovascular than other flavors. Conclusions This study identified different potential associations between e-liquid flavors and the reporting of health symptoms using social media data. The results of this study provide valuable information for further investigation of the health effects associated with different e-liquid flavors.
    Type of Medium: Online Resource
    ISSN: 1438-8871
    URL: Article
    DOI: 10.2196/17496
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2020
    detail.hit.zdb_id: 2028830-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    Novelty Detection and Online Learning for Chunk Data Streams
    Institute of Electrical and Electronics Engineers (IEEE) ; 2021
    In:  IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 43, No. 7 ( 2021-7-1), p. 2400-2412
    Details
    In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers (IEEE), Vol. 43, No. 7 ( 2021-7-1), p. 2400-2412
    Type of Medium: Online Resource
    ISSN: 0162-8828 , 2160-9292 , 1939-3539
    URL: Article
    DOI: 10.1109/TPAMI.2020.2965531
    RVK:
    SQ 1100
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2021
    detail.hit.zdb_id: 2027336-8
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 6
    Online Resource
    Online Resource
    Coarse-to-Fine Localization of Temporal Action Proposals
    Institute of Electrical and Electronics Engineers (IEEE) ; 2020
    In:  IEEE Transactions on Multimedia Vol. 22, No. 6 ( 2020-6), p. 1577-1590
    Details
    In: IEEE Transactions on Multimedia, Institute of Electrical and Electronics Engineers (IEEE), Vol. 22, No. 6 ( 2020-6), p. 1577-1590
    Type of Medium: Online Resource
    ISSN: 1520-9210 , 1941-0077
    URL: Journal
    URL: Article
    DOI: 10.1109/TMM.6046
    DOI: 10.1109/TMM.2019.2943204
    RVK:
    ST 325
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2020
    detail.hit.zdb_id: 2033070-4
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 7
    Online Resource
    Online Resource
    Tactics and Tallies: A Study of the 2016 U.S. Presidential Campaign Using Twitter 'Likes'
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2017
    In:  Proceedings of the International AAAI Conference on Web and Social Media Vol. 11, No. 1 ( 2017-05-03), p. 775-782
    Details
    In: Proceedings of the International AAAI Conference on Web and Social Media, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 11, No. 1 ( 2017-05-03), p. 775-782
    Abstract: We propose a framework to measure, evaluate, and rank campaign effectiveness in the ongoing 2016 U.S. presidential election. Using Twitter data collected from Sept. 2015 to Jan. 2016, we first uncover the tweeting tactics of the candidates and second, using negative binomial regression and exploiting the variations in `likes,' we evaluate the effectiveness of these tactics. Thirdly, we rank the candidates' campaign tactics by calculating the conditional expectation of their generated 'likes.' We show that while Ted Cruz and Marco Rubio put much weight on President Obama, this tactic is not being well received by their supporters. We demonstrate that Hillary Clinton's tactic of linking herself to President Obama resonates well with her supporters but the same is not true for Bernie Sanders. In addition, we show that Donald Trump is a major topic for all the other candidates and that the women issue is equally emphasized in Sanders' campaign as in Clinton's. Finally, we suggest two ways that politicians can use the feedback mechanism in social media to improve their campaign: (1) use feedback from social media to improve campaign tactics within social media; (2) prototype policies and test the public response from the social media.
    Type of Medium: Online Resource
    ISSN: 2334-0770 , 2162-3449
    URL: Article
    DOI: 10.1609/icwsm.v11i1.14978
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2017
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 8
    Online Resource
    Online Resource
    Detecting the Hate Code on Social Media
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2017
    In:  Proceedings of the International AAAI Conference on Web and Social Media Vol. 11, No. 1 ( 2017-05-03), p. 608-611
    Details
    In: Proceedings of the International AAAI Conference on Web and Social Media, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 11, No. 1 ( 2017-05-03), p. 608-611
    Abstract: Social media has become an indispensable part of the everyday lives of millions of people around the world. It provides a platform for expressing opinions and beliefs, communicated to a massive audience. However, this ease with which people can express themselves has also allowed for the large scale spread of propaganda and hate speech. To prevent violating the abuse policies of social media platforms and also to avoid detection by automatic systems like Google’s Conversation AI, racists have begun to use a code (a movement termed Operation Google). This involves substituting references to communities by benign words that seem out of context, in hate filled posts or Tweets. For example, users have used the words Googles and Bings to represent the African-American and Asian communities, respectively. By generating the list of users who post such content, we move a step forward from classifying tweets by allowing us to study the usage pattern of these concentrated set of users.
    Type of Medium: Online Resource
    ISSN: 2334-0770 , 2162-3449
    URL: Article
    DOI: 10.1609/icwsm.v11i1.14921
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2017
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 9
    Online Resource
    Online Resource
    Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test
    Public Library of Science (PLoS) ; 2022
    In:  PLOS Digital Health Vol. 1, No. 6 ( 2022-6-2), p. e0000046-
    Details
    In: PLOS Digital Health, Public Library of Science (PLoS), Vol. 1, No. 6 ( 2022-6-2), p. e0000046-
    Abstract: Early Childhood Caries (ECC) is the most common childhood disease worldwide and a health disparity among underserved children. ECC is preventable and reversible if detected early. However, many children from low-income families encounter barriers to dental care. An at-home caries detection technology could potentially improve access to dental care regardless of patients’ economic status and address the overwhelming prevalence of ECC. Our team has developed a smartphone application (app), AICaries, that uses artificial intelligence (AI)-powered technology to detect caries using children’s teeth photos. We used mixed methods to assess the acceptance, usability, and feasibility of the AICaries app among underserved parent-child dyads. We conducted moderated usability testing (Step 1) with ten parent-child dyads using "Think-aloud" methods to assess the flow and functionality of the app and analyze the data to refine the app and procedures. Next, we conducted unmoderated field testing (Step 2) with 32 parent-child dyads to test the app within their natural environment (home) over two weeks. We administered the System Usability Scale (SUS) and conducted semi-structured individual interviews with parents and conducted thematic analyses. AICaries app received a 78.4 SUS score from the participants, indicating an excellent acceptance. Notably, the majority (78.5%) of parent-taken photos of children’s teeth were satisfactory in quality for detection of caries using the AI app. Parents suggested using community health workers to provide training to parents needing assistance in taking high quality photos of their young child’s teeth. Perceived benefits from using the AICaries app include convenient at-home caries screening, informative on caries risk and education, and engaging family members. Data from this study support future clinical trial that evaluates the real-world impact of using this innovative smartphone app on early detection and prevention of ECC among low-income children.
    Type of Medium: Online Resource
    ISSN: 2767-3170
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.1371/journal.pdig.0000046
    DOI: 10.1371/journal.pdig.0000046.g001
    DOI: 10.1371/journal.pdig.0000046.g002
    DOI: 10.1371/journal.pdig.0000046.g003
    DOI: 10.1371/journal.pdig.0000046.g004
    DOI: 10.1371/journal.pdig.0000046.g005
    DOI: 10.1371/journal.pdig.0000046.g006
    DOI: 10.1371/journal.pdig.0000046.g007
    DOI: 10.1371/journal.pdig.0000046.t001
    DOI: 10.1371/journal.pdig.0000046.t002
    DOI: 10.1371/journal.pdig.0000046.t003
    DOI: 10.1371/journal.pdig.0000046.s001
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2022
    detail.hit.zdb_id: 3106944-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 10
    Online Resource
    Online Resource
    Visual Sentiment Analysis by Attending on Local Image Regions
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2017
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 31, No. 1 ( 2017-02-10)
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 31, No. 1 ( 2017-02-10)
    Abstract: Visual sentiment analysis, which studies the emotional response of humans on visual stimuli such as images and videos, has been an interesting and challenging problem. It tries to understand the high-level content of visual data. The success of current models can be attributed to the development of robust algorithms from computer vision. Most of the existing models try to solve the problem by proposing either robust features or more complex models. In particular, visual features from the whole image or video are the main proposed inputs. Little attention has been paid to local areas, which we believe is pretty relevant to human's emotional response to the whole image. In this work, we study the impact of local image regions on visual sentiment analysis. Our proposed model utilizes the recent studied attention mechanism to jointly discover the relevant local regions and build a sentiment classifier on top of these local regions. The experimental results suggest that 1) our model is capable of automatically discovering sentimental local regions of given images and 2) it outperforms existing state-of-the-art algorithms to visual sentiment analysis.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v31i1.10501
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2017
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...