Real-time detection and sorting of news on microblogging platforms

Due to the increasing popularity of microblog-ging platforms (e.g., Twitter), detecting real-Time news from microblogs (e.g., tweets) has recently drawn a lot of attention. Most of the previous work on this subject detect news by analyzing propagation patterns of microblogs. This approach has two limitations: (i) many non-news microblogs (e.g. marketing activi-Ties) have propagation patterns similar to news microblogs and therefore they can be false-ly reported as news; (ii) using propagation patterns to identify news involves a time de-lay until the pattern is formed, therefore news are not detected in real time. We propose an alternative approach, which, motivated by the necessity of real-Time detection of news, does not rely on propagation of posts. More-over, we propose a real-Time sorting strategy that orders the detected news microblogs us-ing a translational approach. An experimen-Tal evaluation on a large-scale microblogging dataset demonstrates the effectiveness of our approach.postprin

On Left and Right: Understanding the Discourse of Presidential Election in Social Media Communities

As a promising platform for political discourse, social media becomes a battleground for presidential candidates as well as their supporters and opponents. Stance detection is one of the key tasks in the understanding of political discourse. However, existing methods are dominated by supervised techniques, which require labeled data. Previous work on stance detection is largely conducted at the post or user level. Despite that some studies have considered online political communities, they either only select a few communities or assume the stance coherence of these communities. Political party extraction has rarely been addressed explicitly. To address the limitations, we developed an unsupervised learning approach to political party extraction and stance detection from social media discourse. We also analyzed and compared (sub)communities with respect to their characteristics of political stances and parties. We further explored (sub)communities’ shift in political stance after the 2020 US presidential election

Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter.

BACKGROUND: Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States. There are several vaccines that protect against strains of HPV most associated with cervical and other cancers. Thus, HPV vaccination has become an important component of adolescent preventive health care. As media evolves, more information about HPV vaccination is shifting to social media platforms such as Twitter. Health information consumed on social media may be especially influential for segments of society such as younger populations, as well as ethnic and racial minorities. OBJECTIVE: The objectives of our study were to quantify HPV vaccine communication on Twitter, and to develop a novel methodology to improve the collection and analysis of Twitter data. METHODS: We collected Twitter data using 10 keywords related to HPV vaccination from August 1, 2014 to July 31, 2015. Prospective data collection used the Twitter Search API and retrospective data collection used Twitter Firehose. Using a codebook to characterize tweet sentiment and content, we coded a subsample of tweets by hand to develop classification models to code the entire sample using machine learning procedures. We also documented the words in the 140-character tweet text most associated with each keyword. We used chi-square tests, analysis of variance, and nonparametric equality of medians to test for significant differences in tweet characteristic by sentiment. RESULTS: A total of 193,379 English-language tweets were collected, classified, and analyzed. Associated words varied with each keyword, with more positive and preventive words associated with HPV vaccine and more negative words associated with name-brand vaccines. Positive sentiment was the largest type of sentiment in the sample, with 75,393 positive tweets (38.99% of the sample), followed by negative sentiment with 48,940 tweets (25.31% of the sample). Positive and neutral tweets constituted the largest percentage of tweets mentioning prevention or protection (20,425/75,393, 27.09% and 6477/25,110, 25.79%, respectively), compared with only 11.5% of negative tweets (5647/48,940; P CONCLUSIONS: Examining social media to detect health trends, as well as to communicate important health information, is a growing area of research in public health. Understanding the content and implications of conversations that form around HPV vaccination on social media can aid health organizations and health-focused Twitter users in creating a meaningful exchange of ideas and in having a significant impact on vaccine uptake. This area of research is inherently interdisciplinary, and this study supports this movement by applying public health, health communication, and data science approaches to extend methodologies across fields