Assessing the use of social media in physician assistant education.

Objectives: This study aims to assess physician assistant (PA) students\u27 experiences with social media (SM) as a part of their medical education. Methods: The study is split into two phases: Phase 1- A cross-sectional survey emailed to all PA students at four PA school campuses to assess students\u27 prior SM experiences (226 responses, 71.1% response rate); and Phase 2- Inclusion of SM educational resources, via Twitter, within lectures performed at two PA schools. A phase-2 survey assessed students\u27 opinions of educational SM (50 responses, 59.5% response rate) and SM usage was tracked. Results: The phase-1 survey respondents indicated that 97.3% (n=220) use social media; often used as a part of their education, 65% (n=147) informally and 2.7% (n=6) formally incorporated. Students most commonly use Facebook, YouTube, and Instagram, but rarely use Twitter. Currently using SM for medical education was significantly associated with predicting that future PA education will formally include SM [r Conclusions: Many PA students are currently using various forms of social media to augment their education. Most PA students support formal incorporation of social media into their education. PA educators should consider using our data and methods of social media inclusion when designing curricula and while clinically precepting PA students

Trends Prediction Using Social Diffusion Models

The importance of the ability of predict trends in social media has been growing rapidly in the past few years with the growing dominance of social media in our everyday's life. Whereas many works focus on the detection of anomalies in networks, there exist little theoretical work on the prediction of the likelihood of anomalous network pattern to globally spread and become "trends". In this work we present an analytic model the social diffusion dynamics of spreading network patterns. Our proposed method is based on information diffusion models, and is capable of predicting future trends based on the analysis of past social interactions between the community's members. We present an analytic lower bound for the probability that emerging trends would successful spread through the network. We demonstrate our model using two comprehensive social datasets - the "Friends and Family" experiment that was held in MIT for over a year, where the complete activity of 140 users was analyzed, and a financial dataset containing the complete activities of over 1.5 million members of the "eToro" social trading community.Comment: 6 Pages + Appendi

Web 2.0 Technologies in the Software Development Process.

Software engineers must communicate with many different people, likely in different locations, in order to create a successful piece of software. Social media can be used to communicate quickly and efficiently to minimize miscommunications and facilitate collaboration in the software development process. Research in this area has been sparse but significant because initial findings show that social media is being used in innovative ways to improve software development. Surveys of what social media some companies are currently using along with information about new social media systems indicate possible uses for these technologies on future software development projects such as documentation maintenance, employee training, and predicting and thus preventing build failures

Understanding Social Media Users via Attributes and Links

abstract: With the rise of social media, hundreds of millions of people spend countless hours all over the globe on social media to connect, interact, share, and create user-generated data. This rich environment provides tremendous opportunities for many different players to easily and effectively reach out to people, interact with them, influence them, or get their opinions. There are two pieces of information that attract most attention on social media sites, including user preferences and interactions. Businesses and organizations use this information to better understand and therefore provide customized services to social media users. This data can be used for different purposes such as, targeted advertisement, product recommendation, or even opinion mining. Social media sites use this information to better serve their users. Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Why Does China Allow Freer Social Media? Protests Versus Surveillance And Propaganda

In this paper, we document basic facts regarding public debates about controversial political issues on Chinese social media. Our documentation is based on a dataset of 13.2 billion blog posts published on Sina Weibo--the most prominent Chinese microblogging platform--during the 2009-2013 period. Our primary finding is that a shockingly large number of posts on highly sensitive topics were published and circulated on social media. For instance, we find millions of posts discussing protests, and these posts are informative in predicting the occurrence of specific events. We find an even larger number of posts with explicit corruption allegations, and that these posts predict future corruption charges of specific individuals. Our findings challenge a popular view that an authoritarian regime would relentlessly censor or even ban social media. Instead, the interaction of an authoritarian government with social media seems more complex.published_or_final_versio

Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media

Consumer spending is a vital macroeconomic indicator. In this paper we present a novel method for predicting future consumer spending from social media data. In contrast to previous work that largely relied on sentiment analysis, the proposed method models consumer spending from purchase intentions found on social media. Our experiments with time series analysis models and machine-learning regression models reveal utility of this data for making short-term forecasts of consumer spending: for three- and seven-day horizons, prediction variables derived from social media help to improve forecast accuracy by 11% to 18% for all the three models, in comparison to models that used only autoregressive predictors

What changed your mind : the roles of dynamic topics and discourse in argumentation process

In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the in- creasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful -- topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations

Can Conversations on Reddit Forecast Future Economic Uncertainty? An Interpretable Machine Learning Approach

In recent years, social media has become an indispensable source of information through which public attitudes, opinions, and concerns can be studied and quantified. This paper proposes an interpretable machine learning framework for predicting the Equity Market-related Economic Uncertainty Index using features generated from a popular discussion forum on Reddit. Our framework consists of a series of custom preprocessing and analytics methods, including BERTopic for latent topic identification and regularized linear models. Using our framework, we evaluate explanatory models with different configurations over a large corpus of Reddit posts belonging to the personal finance category. Our analysis generates valuable insights about discussion topics on Reddit and their efficacy in accurately predicting future economic uncertainty. The study demonstrates the potential of using social media data and interpretable machine learning to inform economic forecasting research