Facts and Fabrications about Ebola: A Twitter Based Study

Microblogging websites like Twitter have been shown to be immensely useful for spreading information on a global scale within seconds. The detrimental effect, however, of such platforms is that misinformation and rumors are also as likely to spread on the network as credible, verified information. From a public health standpoint, the spread of misinformation creates unnecessary panic for the public. We recently witnessed several such scenarios during the outbreak of Ebola in 2014 [14, 1]. In order to effectively counter the medical misinformation in a timely manner, our goal here is to study the nature of such misinformation and rumors in the United States during fall 2014 when a handful of Ebola cases were confirmed in North America. It is a well known convention on Twitter to use hashtags to give context to a Twitter message (a tweet). In this study, we collected approximately 47M tweets from the Twitter streaming API related to Ebola. Based on hashtags, we propose a method to classify the tweets into two sets: credible and speculative. We analyze these two sets and study how they differ in terms of a number of features extracted from the Twitter API. In conclusion, we infer several interesting differences between the two sets. We outline further potential directions to using this material for monitoring and separating speculative tweets from credible ones, to enable improved public health information.Comment: Appears in SIGKDD BigCHat Workshop 201

Machine Learning and Applications on Social Media Data

The emergence of social media and advances in mobile technology and internethas resulted in constant connectivity across users enabling them to post, share, and engage with content published on the web. Studying and learning from such data aboutusers, and their engagement with content can give insights into the current and emerging trends in society. However, studying social media data comes with its own set ofunique challenges. Social media data is highly unstructured because the content is notcurated to adhere to any formal structure. This makes the process of analyzing the datachallenging. Each message published on social media has Social media data is alsohighly volatile since huge volumes of data is generated every second. In this thesis, wepropose machine learning based algorithms and methodologies to accommodate thesechallenges; and apply the algorithms to solve problems in domains of public health andjournalism.Chapter 1 proposes a new framework to combine the text and user engagementdata to detect trends from social networks.Chapter 2 studies social media data to predict the impact of news events. Thechatter on social media surrounding news events is accurately quantified, and is foundto be the most distinguishing feature between high-impact and low-impact events.Chapter 3 uses topic modeling to discover attitudes and trends about drug abuse

Machine Learning and Applications on Social Media Data

The emergence of social media and advances in mobile technology and internethas resulted in constant connectivity across users enabling them to post, share, and engage with content published on the web. Studying and learning from such data aboutusers, and their engagement with content can give insights into the current and emerging trends in society. However, studying social media data comes with its own set ofunique challenges. Social media data is highly unstructured because the content is notcurated to adhere to any formal structure. This makes the process of analyzing the datachallenging. Each message published on social media has Social media data is alsohighly volatile since huge volumes of data is generated every second. In this thesis, wepropose machine learning based algorithms and methodologies to accommodate thesechallenges; and apply the algorithms to solve problems in domains of public health andjournalism.Chapter 1 proposes a new framework to combine the text and user engagementdata to detect trends from social networks.Chapter 2 studies social media data to predict the impact of news events. Thechatter on social media surrounding news events is accurately quantified, and is foundto be the most distinguishing feature between high-impact and low-impact events.Chapter 3 uses topic modeling to discover attitudes and trends about drug abuse

Detection of Illicit Online Sales of Fentanyls via Twitter

detecting the illicit sales of fentanyl from Twitter feeds

Turbulence et transprot anormal dans les plasmas de tokamak

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Each row is the average representation of all the events in a cluster.

<p>A darker cell represents a higher relative frequency value. The y-axis specifies the number of events in each cluster. Clusters are (top to bottom): high-activity, medium-high medium-low and low.</p

Average histograms of the high activity, medium-high activity, medium-low activity and low activity clusters in our dataset (from left to right and top to bottom).

<p>All histograms include standard deviation bars and were cut-off at 60 second length for better visibility.</p