Topic evolution modeling in social media short texts based on recurrent semantic dependent CRP

2017 ◽  
Author(s):  
Yuhao Zhang ◽  
Wenji Mao ◽  
Daniel Zeng
Keyword(s):  
Social Media ◽  
Topic Evolution ◽  
Evolution Modeling

Hashtag-based topic evolution in social media

2017 ◽  
Vol 20 (6) ◽  
pp. 1527-1549 ◽  
Author(s):  
Md. Hijbul Alam ◽  
Woo-Jong Ryu ◽  
SangKeun Lee
Keyword(s):  
Social Media ◽  
Topic Evolution

scholarly journals Evaluating the cohesion of municipalities’ discourse during the COVID-19 pandemic

2021 ◽  
Author(s):  
Victor Antonio Menuzzo ◽  
André Santanchè ◽  
Luiz Gomes-Jr
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Communication Strategies ◽  
Epidemiological Data ◽  
Future Health ◽  
Health Crisis ◽  
Topic Evolution ◽  
Initial Analysis ◽  
Dirichlet Allocation

Social media has been used as a method to alert and raise awareness among the population to help fight the COVID-19 pandemic. We argue that the discourse of municipalities and their respective mayors may have an influence on the behavior of the population and thus directly impact COVID-19 outcomes. This paper analyzes the diversity and cohesion of these discourses through posts published on Facebook, evaluating (i) diversity of topics discussed, (ii) topic evolution, and (iii) deviation from a central discourse. We also combine this information with epidemiological data to assess impact in the outcomes. In particular, we present two different Latent Dirichlet allocation (LDA) models to analyze how topics are being discussed by municipalities/mayors and compare how cohesion is related to the evolution of the pandemic. Our initial analysis suggests that municipalities tend to employ a unified discourse as a response to the worsening of epidemic outcomes. The results of our study could help to inform governments of better communication strategies in this and future health crisis.

Modeling and Analyzing of Research Topic Evolution Associated with Social Networks of Researchers

2016 ◽  
Vol 7 (3) ◽  
pp. 42-62
Author(s):  
Wei Liang ◽  
Zixian Lu ◽  
Qun Jin ◽  
Yonghua Xiong ◽  
Min Wu
Keyword(s):  
Social Networks ◽  
Research Topic ◽  
Research Trends ◽  
Major Research ◽  
Research Topics ◽  
Academic Literature ◽  
Topic Evolution ◽  
Academic Topic ◽  
Evolution Modeling ◽  
Scientific Collaborations

Research trends keep evolving along the time with certain trackable patterns. Mining academic literature and discovering the latent research trends evolution is an interesting and important problem. Few of previous studies focusing on academic topic evolution modeling have addressed the temporal topic evolution patterns. In addition, researchers' profile and their social networks are valuable complementary to the research trends tracking. In this study, to analyze the underlying research trends evolution along with the scientific collaborations of researchers, a novel temporal research trends evolution model associated with researchers' social networks is proposed and built. Specifically, the detected research topics are classified into different clusters in each timeslot, and the evolution patterns are deduced among these topic clusters. The effectiveness of our approach is evaluated based on a real academic dataset. The experimental results can help users to discover the major research trends for specific fields. Besides, the tracked statuses of the corresponding scientific groups are helpful for searching research trends or finding collaboration opportunities according to researchers' different requirements.

scholarly journals Comment Topic Evolution on a Cancer Institution’s Facebook Page

2017 ◽  
Vol 08 (03) ◽  
pp. 854-865 ◽  
Author(s):  
Li Zhou ◽  
Joseph Plasek ◽  
Ronen Rozenblum ◽  
David Bates ◽  
Chunlei Tang
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Pearson Correlation ◽  
Family Members ◽  
Correlation Coefficients ◽  
Free Text ◽  
Strong Positive Correlation ◽  
Allocation Model ◽  
Positive Correlation ◽  
Topic Evolution

SummaryObjectives: Our goal was to identify and track the evolution of the topics discussed in free-text comments on a cancer institution’s social media page.Methods: We utilized the Latent Dirichlet Allocation model to extract ten topics from free-text comments on a cancer research institution’s Facebook™ page between January 1, 2009, and June 30, 2014. We calculated Pearson correlation coefficients between the comment categories to demonstrate topic intensity evolution.Results: A total of 4,335 comments were included in this study, from which ten topics were identified: greetings (17.3%), comments about the cancer institution (16.7%), blessings (10.9%), time (10.7%), treatment (9.3%), expressions of optimism (7.9%), tumor (7.5%), father figure (6.3%), and other family members & friends (8.2%), leaving 5.1% of comments unclassified. The comment distributions reveal an overall increasing trend during the study period. We discovered a strong positive correlation between greetings and other family members & friends (r=0.88; p<0.001), a positive correlation between blessings and the cancer institution (r=0.65; p<0.05), and a negative correlation between blessings and greetings (r=–0.70; p<0.05).Conclusions: A cancer institution’s social media platform can provide emotional support to patients and family members. Topic analysis may help institutions better identify and support the needs (emotional, instrumental, and social) of their community and influence their social media strategy.Citation: Tang C, Zhou L, Plasek J, Rozenblum R, Bates D. Comment Topic Evolution on a Cancer Institution’s Facebook Page. Appl Clin Inform 2017; 8: 854–865 https://doi.org/10.4338/ACI-2017-04-RA-0055

Where Social Media Meets AAC

ASHA Leader ◽  
2015 ◽  
Vol 20 (7) ◽  
Author(s):  
Vicki Clarke
Keyword(s):  
Social Media
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