Digital haemophilia: Insights into the use of social media for haemophilia care, research and advocacy.
Digital haemophilia: Insights into the use of social media for haemophilia care, research and advocacy.
复制标题
DOI：
10.1111/hae.14510
复制
发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Samelson-Jones BJ
中科院分区：
文献类型：
--
作者：
Chen R;Muralidharan K;Samelson-Jones BJ
Twitter上的血友病社区是多样化的，包括倡导团体、患者、医生、研究人员和其他用户。然而，该社区的范围尚未确定，有关有效参与该社区的数据有限。评估Twitter上活跃在血友病社区的用户类型，以及血友病相关推文中的主要主题。2019年9月至2021年9月期间的49512条推文使用正则表达式进行了分类。人工分析了分类推文的一个子集，以确定流行的讨论主题。在帖子数量最多的250个用户中，最大的用户类别是支持和倡导团体、出血性疾病患者和医疗保健提供者。推文的最大主题类别是基因治疗、受污染血友病血液制品、血友病研究、血友病临床管理和COVID-19。虽然错误信息很少见，但公众对血友病的负面和不正确看法仍然存在。我们的研究结果展示了Twitter在血友病社区中用于患者护理、研究和宣传目的的有效使用模式。
The hemophilia community on Twitter is diverse, consisting of advocacy groups, patients, physicians, researchers, and other users. However, the scope of this community is uncharacterized, and limited data are available regarding effective participation in this community. To assess the types of users active in the hemophilia community on Twitter, as well as major themes present in hemophilia-related tweets. 49,512 tweets between September 2019 and September 2021 were classified using regular expressions. A subset of the classified tweets was manually analyzed to identify prevalent discussion themes. Among the top 250 users by post count, the largest categories of users were support and advocacy groups, people with bleeding disorders, and healthcare providers. The largest thematic categories of tweets were gene therapy, contaminated hemophilia blood products, hemophilia research, clinical management of hemophilia, and COVID-19. While misinformation was rare, negative and incorrect perceptions of hemophilia were present among the general public. Our results demonstrate patterns of effective Twitter usage for patient care, research, and advocacy purposes among the hemophilia community.
登录
查看更多内容
DOI：
10.1007/s11764-013-0279-5
发表时间：
2013-09
期刊：
Journal of cancer survivorship : research and practice
影响因子：
--
作者：
Valle CG;Tate DF;Mayer DK;Allicock M;Cai J
通讯作者：
Cai J
影响因子：
8.6
作者：
Burel G;Farrell T;Alani H
通讯作者：
Alani H
映射医师Twitter网络：描述它们如何作为理解连接性，信息流和消息扩散的第一步。
影响因子：
7.4
作者：
Mishori R;Singh LO;Levy B;Newport C
通讯作者：
Newport C
影响因子：
2.6
作者：
Crowson, Matthew G.;Tucci, Debara L.;Kaylie, David
通讯作者：
Kaylie, David
影响因子：
3.2
作者：
Chiu, Yu-Chan;Hsieh, Yu-Ling
通讯作者：
Hsieh, Yu-Ling