Visual Analysis of Topic Competition on Social Media

By Panpan Xu, Yingcai Wu, Enxun Wei, Tai-Quan Peng, Shixia Liu, Jonathan J. H. Zhu, Huamin Qu. 2013

153 citations recorded by the source corpus.

Subfields: Communication, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

1. Shixia Liu
2. Jian Pei
3. Furu Wei

In Computer Vision and Pattern Recognition

1. Shixia Liu
2. Jun Zhu
3. Mengchen Liu
4. Yingcai Wu
5. Chongxuan Li
6. Weiwei Cui
7. Ronghua Liang
8. Guodao Sun

In Statistical and Nonlinear Physics

1. Shixia Liu
2. Mengchen Liu
3. Jiaxin Shi
4. Yingcai Wu
5. Weiwei Cui
6. Chongxuan Li
7. Jun Zhu
8. Jian Pei

Citations in the corpus

• Towards Better Analysis of Deep Convolutional Neural Networks (2016, background): “sualization, researchers have achieved a great deal of success in modeling domain-specific data as a DAG. Typical data includes dynamic relationships between entities [34, 50, 51], temporal topic data [10, 14, 47, 56], temporal event sequences [54], evolving egocentric network [55], and the informati”
• How Hierarchical Topics Evolve in Large Text Corpora (2014, methodology): “Numerous methods [14, 42, 41] leverage a river metaphor to convey evolving topics over time.”
• An Uncertainty-Aware Approach for Exploratory Microblog Retrieval (2015, methodology): “The methods applied include event detection [30], topic extraction and analysis [25, 40, 50], information diffusion [8, 52], sentiment analysis [47, 48], and revenue/stock prediction [28, 34].”
• Online Visual Analytics of Text Streams (2015, methodology): “A visual analysis system was designed by Xu et al. [42] to allow analysts to interactively explore and understand the dynamic competition relationships among topics.”
• A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges (2013, background): “More recently, Xu et al.([85]) studied the competition among topics through information diffusion on social media as well as the impact of opinion leaders on competition.”
• EvoRiver: Visual Analysis of Topic Coopetition on Social Media (2014, methodology): “Both approaches [13, 44] employ a composite visual design that draws threads above the strips of a streamgraph.”
• StoryFlow: Tracking the Evolution of Stories (2013, background): “[47] studied the problem of temporal topic competition over time.”
• A survey on information visualization: recent advances and challenges (2014, background): “Moreover, we see average users, including consumers, citizens, and patients, examine public data such as product specifications, blogs, and online communities to choose products to buy [39], decide issues to vote on [163], and seek health-related information [23].”