Related papers: TwitterCrowds: Techniques for Exploring Topic and …

Social networks play a fundamental role in propagation of information and news. Characterizing the content of the messages becomes vital for different tasks, like breaking news detection, personalized message recommendation, fake users…

Recently, a new window to explore tweet data has been opened in TExVis tool through visualizing the relations between the frequent keywords. However, timeline exploration of tweet data, not present in TExVis, could play a critical factor in…

Tag clouds provide an aggregate of tag-usage statistics. They are typically sent as in-line HTML to browsers. However, display mechanisms suited for ordinary text are not ideal for tags, because font sizes may vary widely on a line. As…

In Twitter, and other microblogging services, the generation of new content by the crowd is often biased towards immediacy: what is happening now. Prompted by the propagation of commentary and information through multiple mediums, users on…

Social networks are the main resources to gather information about people's opinion and sentiments towards different topics as they spend hours daily on social media and share their opinion. In this technical paper, we show the application…

Collaborative tagging has recently attracted the attention of both industry and academia due to the popularity of content-sharing systems such as CiteULike, del.icio.us, and Flickr. These systems give users the opportunity to add data items…

Folksonomy is said to provide a democratic tagging system that reflects the opinions of the general public, but it is not a classification system and it is hard to make sense of. It would be necessary to share a representation of contexts…

The exponential growth of social media has created an urgent need for automated systems to analyze unstructured public sentiment in real time. This study compares a traditional Logistic Regression model using TF-IDF features with a deep…

The increasing popularity of Twitter and other microblogs makes improved trustworthiness and relevance assessment of microblogs evermore important. We propose a method of ranking of tweets considering trustworthiness and content based…

A sentiment analysis system powered by machine learning was created in this study to improve real-time social network public opinion monitoring. For sophisticated sentiment identification, the suggested approach combines cutting-edge…

We present a highly effective unsupervised framework for detecting the stance of prolific Twitter users with respect to controversial topics. In particular, we use dimensionality reduction to project users onto a low-dimensional space,…

The present study proposes a novel method of trend detection and visualization - more specifically, modeling the change in a topic over time. Where current models used for the identification and visualization of trends only convey the…

Affective sharing within groups strengthens coordination and empathy, leads to better health outcomes, and increases productivity and performance. Existing tools for affective sharing face one main challenge: creating a representation of…

An important part of the information gathering and data analysis is to find out what people think about, either a product or an entity. Twitter is an opinion rich social networking site. The posts or tweets from this data can be used for…

Traditional methods of collecting user feedback through transit surveys are often time-consuming, resource intensive, and costly. In this paper, we propose a novel NLP-based framework that harnesses the vast, abundant, and inexpensive data…

Tweet classification has attracted considerable attention recently. Most of the existing work on tweet classification focuses on topic classification, which classifies tweets into several predefined categories, and sentiment classification,…

The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different…

We introduce the problem of visual hashtag discovery for infographics: extracting visual elements from an infographic that are diagnostic of its topic. Given an infographic as input, our computational approach automatically outputs textual…

This paper introduces a temporal framework for detecting and clustering emergent and viral topics on social networks. Endogenous and exogenous influence on developing viral content is explored using a clustering method based on the a user's…

Extracting topics from large collections of unstructured text-documents has become a central task in current NLP applications and algorithms like NMF, LDA as well as their generalizations are the well-established current state of the art.…