A Family of Discrete Distributions Defined via Their Factorial Moments

Kemp, Adrienne W.; Kemp, C. D.

doi:10.1080/03610917408548331

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As of July 2022, the global social media user base reached 59% of the world's total population [5]. Social media is omnipresent, evolving quickly, and increasingly affecting people's lives and health behaviors.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Monetary Incentives on Health Care Social Media Content: Study Based on Topic Modeling and Sentiment Analysis

Maleki¹,

Padmanabhan²,

Dutta³

2023

J Med Internet Res

View full text Add to dashboard Cite

Background While there is high-quality online health information, a lot of recent work has unfortunately highlighted significant issues with the health content on social media platforms (eg, fake news and misinformation), the consequences of which are severe in health care. One solution is to investigate methods that encourage users to post high-quality content. Objective Incentives have been shown to work in many domains, but until recently, there was no method to provide financial incentives easily on social media for users to generate high-quality content. This study investigates the following question: What effect does the provision of incentives have on the creation of social media health care content? Methods We analyzed 8328 health-related posts from an incentive-based platform (Steemit) and 1682 health-related posts from a traditional platform (Reddit). Using topic modeling and sentiment analysis–based methods in machine learning, we analyzed these posts across the following 3 dimensions: (1) emotion and language style using the IBM Watson Tone Analyzer service, (2) topic similarity and difference from contrastive topic modeling, and (3) the extent to which posts resemble clickbait. We also conducted a survey using 276 Amazon Mechanical Turk (MTurk) users and asked them to score the quality of Steemit and Reddit posts. Results Using the Watson Tone Analyzer in a sample of 2000 posts from Steemit and Reddit, we found that more than double the number of Steemit posts had a confident language style compared with Reddit posts (77 vs 30). Moreover, 50% more Steemit posts had analytical content and 33% less Steemit posts had a tentative language style compared with Reddit posts (619 vs 430 and 416 vs 627, respectively). Furthermore, more than double the number of Steemit posts were considered joyful compared with Reddit posts (435 vs 200), whereas negative posts (eg, sadness, fear, and anger) were 33% less on Steemit than on Reddit (384 vs 569). Contrastive topic discovery showed that only 20% (2/10) of topics were common, and Steemit had more unique topics than Reddit (5 vs 3). Qualitatively, Steemit topics were more informational, while Reddit topics involved discussions, which may explain some of the quantitative differences. Manual labeling marked more Steemit headlines as clickbait than Reddit headlines (66 vs 26), and machine learning model labeling consistently identified a higher percentage of Steemit headlines as clickbait than Reddit headlines. In the survey, MTurk users said that at least 57% of Steemit posts had better quality than Reddit posts, and they were at least 52% more likely to like and comment on Steemit posts than Reddit posts. Conclusions It is becoming increasingly important to ensure high-quality health content on social media; therefore, incentive-based social media could be important in the design of next-generation social platforms for health information.

show abstract

Section: Introductionmentioning

confidence: 99%

The Effect of Monetary Incentives on Health Care Social Media Content: Study Based on Topic Modeling and Sentiment Analysis

Maleki¹,

Padmanabhan²,

Dutta³

2023

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Numerous generalizations and extensions of these models exist and many have been solved analytically, e.g., the leaky two-state model where the promoter switches between two states with different levels of activity [22][23][24], multi-state models that consider a promoter with more than two states [27,31,34,35,52], models with auto-regulation [25,26,30,33], etc. All of these models result in probability generating functions that are related to the Kemp families of distributions, which have been derived using various urn models of contagion and population heterogeneity, and as compound or mixture distributions [53][54][55]. Notably, in each of their applications, the urn model has a distinct design, which is systematically developed to capture the physical characteristics of the natural system under study.…”

Section: Introductionmentioning

confidence: 99%

Urn models for stochastic gene expression yield intuitive insights into the probability distributions of single-cell mRNA and protein counts

Choudhary¹,

Narang²

2020

Phys. Biol.

View full text Add to dashboard Cite

Fitting the probability mass functions from analytical solutions of stochastic models of gene expression to the single-cell count distributions of mRNA and protein molecules can yield valuable insights into mechanisms underlying gene expression. Solutions of chemical master equations are available for various kinetic schemes but, even for the basic ON-OFF genetic switch, they take complex forms with generating functions given as hypergeometric functions. Interpretation of gene expression dynamics in terms of bursts is not consistent with the complete range of parameters for these functions. Physical insights into the probability mass functions are essential to ensure proper interpretations but are lacking for models considering genetic switches. To fill this gap, we develop urn models for stochastic gene expression. We sample RNA polymerases or ribosomes from a master urn, which represents the cytosol, and assign them to recipient urns of two or more colors, which represent time intervals in which no switching occurs. Colors of the recipient urns represent sub-systems of the promoter states, and the assignments to urns of a specific color represent gene expression. We use elementary principles of discrete probability theory to solve a range of kinetic models without feedback, including the Peccoud-Ycart model, the Shahrezaei-Swain model, and models with an arbitrary number of promoter states. In the last case, we obtain a novel result for the protein distribution. For activated genes, we show that transcriptional lapses, which are events of gene inactivation for short time intervals separated by long active intervals, quantify the transcriptional dynamics better than bursts. We show that the intuition gained from our urn models may also be useful in understanding existing solutions for models with feedback. We contrast our models with urn models for related distributions, discuss a generalization of the Delaporte distribution for single-cell data analysis, and highlight the limitations of our models.

show abstract

A Family of Discrete Distributions Defined via Their Factorial Moments

Cited by 2 publications

References 0 publications

The Effect of Monetary Incentives on Health Care Social Media Content: Study Based on Topic Modeling and Sentiment Analysis

The Effect of Monetary Incentives on Health Care Social Media Content: Study Based on Topic Modeling and Sentiment Analysis

Urn models for stochastic gene expression yield intuitive insights into the probability distributions of single-cell mRNA and protein counts

Contact Info

Product

Resources

About