Learning similarity metrics for event identification in social media

By Hila Becker, Mor Naaman, Luis Gravano. Web Search and Data Mining, 2010

425 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d7f72ac79527b9e3fd612fc5202b5c83721d9d52

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In Artificial Intelligence

Citations in the corpus

Social big data: Recent achievements and new challenges (2015, background): “• Identifying events [148] in social media documents via clustering and using similarity metric learning approaches to produce highquality clustering results [149] .”
Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? (2015, background): “texture image retrieval [8], webpage clustering [9] or event identification in social media [10].”
External validity of sentiment mining reports: Can current methods identify demographic biases, event biases, and manipulation of reviews? (2014, methodology): “[63] described the identification of large events as clustering problems.”
Learning similarity functions for topic detection in online reputation monitoring (2014, background): “Becker et al.[7] analyzed the effectiveness of combining meta-data information (tags, time, location, etc.) to textual data for clustering of social media documents (e.g., Flickr images) according to previously unknown real-life events.”
Event Summarization Using Tweets (2011, background): “Since then a lot of research works have focused on detection of novel events on Twitter [10] and other social media websites [2] such as Flickr and Youtube.”
Hip and trendy: Characterizing emerging trends on Twitter (2011, methodology): “Note again that other methods exist for trend detection that may associate content with trends not only by simple term matching as we do here (Becker et al., 2010).”
Harvesting ambient geospatial information from social media feeds (2011, background): “Taking advantage of the annotation content of these datasets provides new analysis opportunities, ranging from (Kennedy et al. 2007), and even identifying events and corresponding social media documents (Becker et al. 2010).”
Semantics-driven Event Clustering in Twitter Feeds (2015, methodology): “were among the first to implement this idea by clustering a Flickr photo collection [2, 3].”
Beyond Trending Topics: Real-World Event Identification on Twitter (2011, background): “Other research considers event identification in other social media data, such as Flickr [3, 7], where structured context features (e.”