Hip and trendy: Characterizing emerging trends on Twitter

By Mor Naaman, Hila Becker, Luis Gravano. J. Assoc. Inf. Sci. Technol., 2011

338 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/aedca557e79d36d4c3f81f76a247a98f1cfffcc4

How researchers see this paper

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

In Statistical and Nonlinear Physics

1. Meredith Ringel Morris
2. Jaime Teevan
3. Jacob Eisenstein
4. Ciro Cattuto
5. Luis Gravano
6. Mor Naaman
7. Daniel J. Liebling
8. Susan T. Dumais

In Transportation

1. Jacob Eisenstein
2. Luis Gravano
3. Mor Naaman
4. José J. Ramasco
5. Hila Becker

Citations in the corpus

• What to do about bad language on the internet (2013, background): “Even within a single language — setting aside issues of code-switching (Paolillo, 1996) — Twitter and other social media can contain registers ranging from hash-tag wordplay (Naaman et al., 2011) to the official pronouncements of the British Monarchy.”
• Investigating the role of social media marketing on value co-creation and engagement: An empirical study in China and Hong Kong (2020, background): “Finally, trendiness refers to the extent to which a brand communicates up-to-date and trendy information (i.e. current ‘hot topics’) relevant to the brand (Naaman et al., 2011).”
• Dynamical classes of collective attention in twitter (2011, background): “The lack of a clear distinction between endogenous and exogenous information flow in Twitter means that the number of classes, the possible functional shapes of the popularity profiles, and even the importance of the endogenous/exogenous distinction are all far from clear [9].”
• Beyond Trending Topics: Real-World Event Identification on Twitter (2011, background): “These interactions might be different between events, Twitter-centric activities, and other non-event messages [4].”
• Towards Supporting Search over Trending Events with Social Media (2013, background): “Naaman, Becker, and Gravano (2011) present a more detailed taxonomy, separating trends into exogenous (breaking news, broadcast events, holidays, and local events) and endogenous (memes, retweets, and fan activity) events and identifying temporal, content, and other features characteristic of…”
• Improving LDA topic models for microblogs via tweet pooling and automatic labeling (2013, background): “Burst-score wise Pooling: A trend on Twitter [7] (sometimes referred to as a trending topic) consists of one or more terms and a time period, such that the volume of messages posted for the terms in the time period exceeds some expected level of activity.”