Finding high-quality content in social media

By Eugene Agichtein, Carlos Castillo, Debora Donato, Aristides Gionis, Gilad Mishne. Web Search and Data Mining, 2008

1,437 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/ea895f91972b97d6d62ecf362effe1afcf76680b

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Citations in the corpus

Finding the right facts in the crowd: factoid question answering over social media (2008, background): “[2] explored user interaction and content-based lexical features to identify high-quality content, but did not address how to retrieve relevant answers.”
Causal discovery in social media using quasi-experimental designs (2010, background): “Researchers have tried to identify high-quality content in social media [2], and to identify attributes that predict answer quality in question and answer platforms [1, 9].”
Identity and Opinion: A Randomized Experiment (2014, neutral): “Exploiting these signals using machine learning techniques can help predict future content interactions, or the editorially determined quality of user-generated content (Agichtein et al. 2008).”
Recommender Systems (2016, positive): “Methods for leveraging the personalized PageRank algorithm in various forms of social recommender systems are discussed in [16, 81, 350, 602, 640, 663].”
SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums (2019, methodology): “Agichtein et al. (2008) looked for high-quality answers using PageRank and HITS, in addition to intrinsic content quality, e.g., punctuation and typos, syntactic and semantic complexity, and grammaticality.”
Social Media Ethnography: The Digital Researcher in a Messy Web (2012, methodology): “The two main methods used to date are web content analysis of large data sets drawn from microblogging and other social media sites (Agichtein et al., 2008; Honeycutt and Herring, 2009; Kwak et al., 2010; Oulasvirta et al., 2010) and social network analysis (Gilbert and Karahalios, 2009; Java et…”
Information credibility on twitter (2011, background): “Many of the features follow previous works including [1, 2, 12, 26].”
Identifying topical authorities in microblogs (2011, methodology): “[1] used the Community Question Answering dataset and extracted several graph features such as the degree distribution of users and their PageRank, hubs and authority scores to model a user’s relative importance based on their network ties.”
Tampering with Twitter’s Sample API (2018, background): “text of political opinion formation [4], crisis management [5, 6], information quality [7],”
Enhancing credibility judgment of web search results (2011, methodology): “Agichtein et al. evaluated authority and hub scores of users by using a graph structure representing the interactions between the users and then evaluated the quality of answers on the basis of those users’ scores [2].”
Characterizing Online Discussion Using Coarse Discourse Sequences (2017, background): “2008; Hong and Davison 2009) or characterizing the types and quantity of Q&A content on different community platforms (Agichtein et al. 2008; Morris, Teevan, and Panovich 2010).”
Dealing with Input Noise in Statistical Machine Translation (2012, methodology): “However, real-life texts are poorly related with this controlled scenario in terms of literary quality (Agichtein et al., 2008; Subramaniam et al., 2009).”
Detecting Information-Dense Texts in Multiple News Domains (2014, background): “Prior work has shown that such features capture aspects of text readability and coherence (Barzilay and Lapata 2008; Lin, Ng, and Kan 2011), but no prior work has tested the extent to which such features correlate with writing style.”
Facts or friends?: distinguishing informational and conversational questions in social Q&A sites (2009, background): “, [1], [2], [9]), we test the extent to which humans are able to agree when classifying random questions across several Q&A sites.”
Learning similarity metrics for event identification in social media (2010, background): “Several efforts have focused on extracting high-quality information from social media [1, 4, 24, 27, 31].”
A Comprehensive Survey and Classification of Approaches for Community Question Answering (2016, background): “This negative development is highly related to the growing amount of low-quality content created by undesired groups of users (i.e., help vampires, noobs, and reputation collectors).”
A review of volunteered geographic information quality assessment methods (2017, methodology): “Text Content Quality Agichtein et al. (2008) describe a generic method for all text-based social media data.”