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Found 9 papers, 0 papers with code
Klout Topics for Modeling Interests and Expertise of Users Across Social Networks
This paper presents Klout Topics, a lightweight ontology to describe social media users' topics of interest and expertise.
Analyzing users' sentiment towards popular consumer industries and brands on Twitter
In this paper, we analyze the opinion of 19M Twitter users towards 62 popular industries, encompassing 12, 898 enterprise and consumer brands, as well as associated subject matter topics, via sentiment analysis of 330M tweets over a period spanning a month.
Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media
In this paper, we describe the Lithium Natural Language Processing (NLP) system - a resource-constrained, high- throughput and language-agnostic system for information extraction from noisy user generated text on social media.
Global Entity Ranking Across Multiple Languages
We present work on building a global long-tailed ranking of entities across multiple languages using Wikipedia and Freebase knowledge bases.
High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data
The Entity Disambiguation and Linking (EDL) task matches entity mentions in text to a unique Knowledge Base (KB) identifier such as a Wikipedia or Freebase id.
DAWT: Densely Annotated Wikipedia Texts across multiple languages
In addition to the main dataset, we open up several derived datasets including mention entity co-occurrence counts and entity embeddings, as well as mappings between Freebase ids and Wikidata item ids.
Mining Half a Billion Topical Experts Across Multiple Social Networks
Here we present the first study that combines data from four major social networks -- Twitter, Facebook, Google+ and LinkedIn, along with the Wikipedia graph and internet webpage text and metadata, to rank topical experts across the global population of users.
Information Retrieval Social and Information Networks
Actionable and Political Text Classification using Word Embeddings and LSTM
We build models for over 30 different languages for actionability, and most of the models achieve accuracy around 85%, with some reaching over 90% accuracy.
When-To-Post on Social Networks
To understand the complexity of the problem, we examine user behavior in terms of post-to-reaction times, and compare cross-network and cross-city weekly reaction behavior for users in different cities, on both Twitter and Facebook.
Social and Information Networks H.1.2; J.4
