TwitterRank: finding topic-sensitive influential twitterers

By Jianshu Weng, Ee-Peng Lim, Jing Jiang, Qi He. Web Search and Data Mining, 2010

2,098 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b9f971d95065ae64edbd3593de718d4efe473abd

How researchers see this paper

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

In Information Systems

1. Huan Liu
2. Fred Morstatter
3. Jianyong Wang
4. Carlos Castillo
5. Jiawei Han
6. Jiliang Tang
7. Xia Hu
8. J´erˆome Kunegis

In Statistical and Nonlinear Physics

1. Jure Leskovec
2. Huan Liu
3. Linyuan Lu
4. Duanbing Chen
5. Tao Zhou
6. Krishna P. Gummadi
7. Carlos Castillo
8. David F. Gleich

Citations in the corpus

• Political Polarization on the Digital Sphere: A Cross-platform, Over-time Analysis of Interactional, Positional, and Affective Polarization on Social Media (2020, background): “Reflecting Twitter’s ambiguous profile, the existing literature yields conflicting findings regarding its tendency toward homophily and polarization (e.g., Kwak et al., 2010 detected little homophily; Weng et al., 2010; Hong & Kim, 2016; Colleoni et al., 2014 found the opposite).”
• Discussion Graphs: Putting Social Media Analysis in Context (2014, methodology): “For example, social media analysis methods have been used in social search and ranking (Weng et al. 2010), recommender systems (Konstas, Stathopoulos, and Jose 2009; Feng and Wang 2012), media summarization (Lin et al.”
• Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, background): “Discovering the topics of interest for a particular Twitter user allows for recommending and searching Twitter users based on their topics of interest [54].”
• Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science (2015, background): “, topic interest [30], political alignment [31], happiness [32], well-being [33], and language [34].”
• Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition (2012, background): “Recently, researchers have applied LDA topic modeling to social media data to summarize and categorize Tweets [39] and find influential users [35].”
• Information diffusion in online social networks: a survey (2013, methodology): “[49] also develop a topic-sensitive version of the Page Rank algorithm dedicated to Twitter, TwitterRank.”
• Twitter's Glass Ceiling: The Effect of Perceived Gender on Online Visibility (2016, background): “User behaviors affect attention: for instance, users who post more or post about certain topics may attract more attention online (Romero et al. 2011; Wu et al. 2011; Weng et al. 2010).”
• Topic Modeling for Short Texts with Auxiliary Word Embeddings (2016, background): “Conventional topic modeling techniques, e.g., pLSA and LDA, are widely used to infer latent topical structure from text corpus [2, 12].”
• What is Twitter, a social network or a news media? (2010, background): “have reported that two users who follow reciprocally share topical interests by mining their 50 thousands links [38].”
• Improving LDA topic models for microblogs via tweet pooling and automatic labeling (2013, methodology): “Author-wise Pooling: Pooling tweets according to author is a standard away of aggregating Twitter data [13, 5] and shown to be superior to unpooled Tweets.”
• Influence and passivity in social media (2010, background): “On the other hand, Weng et al [ 5 ] have proposed a topic-sensitive PageRank measure for influence in Twitter.”
• Harvesting ambient geospatial information from social media feeds (2011, background): “…between the physical location of tweeting individuals relative to the reported event and their network importance (locals gain importance in the network when reporting about a local event), as well as that local networks tend to become denser when addressing local events (Yardi and Boyd 2010).”
• Bad news travel fast: a content-based analysis of interestingness on Twitter (2011, methodology): “using PageRank, the number of followers, the number of retweets and trending topics [4, 6, 9, 13, 14, 15].”
• An Uncertainty-Aware Approach for Exploratory Microblog Retrieval (2015, methodology): “This need is consistent with the findings of previous research [16, 46].”
• A biterm topic model for short texts (2013, methodology): “information, and then train conventional topic models [32, 35].”
• PageRank beyond the Web (2014, methodology): “PageRank in social networks: BuddyRank, TwitterRank.”