What is Tumblr: a statistical overview and comparison

@article{Chang2014WhatIT,
  title={What is Tumblr: a statistical overview and comparison},
  author={Yi Chang and Lei Tang and Yoshiyuki Inagaki and Yan Liu},
  journal={ArXiv},
  year={2014},
  volume={abs/1403.5206}
}
Tumblr, as one of the most popular microblogging platforms, has gained momentum recently. [] Key Result This work serves as an early snapshot of Tumblr that later work can leverage.

Spider and the Flies : Focused Crawling on Tumblr to Detect Hate Promoting Communities

TLDR
A topic based web crawler primarily consisting of multiple phases: training a text classifier model consisting examples of only hate promoting users, extracting posts of an unknown tumblr micro-blogger, classifying hate promoting bloggers based on their activity feeds and performing a social network analysis on connected extremist bloggers is proposed.

Uncovering Hidden Communities of Extremist Micro-Bloggers : A Case Study of Jihadist Groups on Tumblr

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This work proposes a topical crawler based approach performing several tasks: searching for a blogger, computing its similarity against exemplary documents, filtering hate promoting bloggers, navigating through links to other bloggers and managing a queue of such bloggers for social network analysis.

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The breadth of topics covered by social media researchers, which allows us to understand popular online platforms, is highlighted, and practical barriers to research on the Tumblr platform including lack of metadata and access to big data are identified.

Measurement and Modeling of Tumblr Traffic

TLDR
This work uses a combination of active and passive approaches to network traffic measurement, and develops and calibrates a synthetic workload model for Tumblr network traffic.

Tumblr Blog Recommendation with Boosted Inductive Matrix Completion

TLDR
A novel boosted inductive matrix completion method (BIMC) for blog recommendation using an additive low-rank model for user-blog preferences consisting of two components; one component captures the low- rank structure of follow relationships and the other captures the latent structure using side-information.

Deconstructing Diffusion on Tumblr: Structural and Temporal Aspects

TLDR
This paper examines cascade networks on Tumblr, recreated from the series of diffusion events, and analyses them from structural and temporal perspectives to achieve a cascade construction model that create cascade networks, overcoming problems of a lack of contextual information and missing/degraded data.

Libraries and Tumblr: a quantitative analysis

Purpose – This study aims to determine how Tumblr is being used by libraries and special collections/archives in the USA through quantitative analysis. Design/methodology/approach – Data on library

Influence and Sentiment Homophily on Twitter

TLDR
An empirical study that combines existing Graph Clustering and Sentiment Analysis techniques for reasoning about Sentiment dynamics at cluster level and analyzing the role of Social Influence on Sentiment contagion, based on a large dataset extracted from Twitter during the 2014 FIFA World Cup is presented.

Leveraging Blogging Activity on Tumblr to Infer Demographics and Interests of Users for Advertising Purposes

TLDR
This paper proposes a novel semi-supervised neural language model for categorization of Tumblr content, trained on a large-scale data set consisting of 6.8 billion user posts, with a very limited amount of categorized keywords, and was shown to have superior performance over the baseline models.
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