To what extent does our online activity reveal who we are? Recent research has
demonstrated that the digital traces left by individuals as they browse and interact with …

Background A growing amount of health research uses social media data. Those critical of
social media research often cite that it may be unrepresentative of the population; however …

Advancements in information and communication technology, and online web users have
given attention to the virtual representation of each user, which is crucial for effective service …

Short text clustering is a challenging problem due to its sparseness of text representation.
Here we propose a flexible Self-Taught Convolutional neural network framework for Short …

In recent years, online social networking has revolutionized interpersonal communication.
The newer research on language analysis in social media has been increasingly focusing …

Social media content can be used as a complementary source to the traditional methods for
extracting and studying collective social attributes. This study focuses on the prediction of …

Most modern Information Extraction (IE) systems are implemented as sequential taggers and
only model local dependencies. Non-local and non-sequential context is, however, a …

At the time of writing, the ongoing pandemic of coronavirus disease (COVID-19) has caused
severe impacts on society, economy and people's daily lives. People constantly express …

The vast availability of textual data on social media has led to an interest in algorithms to
predict user attributes such as gender based on the user's writing. These methods are …

Putting one's feelings into words (also called affect labeling) can attenuate positive and
negative emotions. Here, we track the evolution of specific emotions for 74,487 Twitter users …