You are where you tweet

Cheng, Zhiyuan; Caverlee, James; Lee, Kyumin

doi:10.1145/1871437.1871535

Cited by 803 publications

(85 citation statements)

References 10 publications

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“…Approximately 75% of the locations obtained were determined using the CLAVIN geo-location extraction algorithm, therefore, only a small percentage of the total tweets contained an embedded GPS location. Again, this result is consistent with other studies (e.g., Cheng et al 2010, Lee et al 2013). The community cast more than 70,000 votes and verified over 4,500 tweets.…”

Section: Discussionsupporting

confidence: 93%

Determining the Accuracy of Crowdsourced Tweet Verification for Auroral Research

Case¹,

MacDonald²,

McCloat³

et al. 2016

CSTP

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The Aurorasaurus project harnesses volunteer crowdsourcing to identify sightings of an aurora (the "northern/southern lights") posted by citizen scientists on Twitter. Previous studies have demonstrated that aurora sightings can be mined from Twitter with the caveat that there is a large background level of non-sighting tweets, especially during periods of low auroral activity. Aurorasaurus attempts to mitigate this, and thus increase the quality of its Twitter sighting data, by using volunteers to sift through a pre-filtered list of geolocated tweets to verify real-time aurora sightings. In this study, the current implementation of this crowdsourced verification system, including the process of geolocating tweets, is described and its accuracy (which, overall, is found to be 68.4%) is determined. The findings suggest that citizen science volunteers are able to accurately filter out unrelated, spam-like, Twitter data but struggle when filtering out somewhat related, yet undesired, data. The citizen scientists particularly struggle with determining the real-time nature of the sightings, so care must be taken when relying on crowdsourced identification.

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Section: Discussionsupporting

confidence: 93%

Determining the Accuracy of Crowdsourced Tweet Verification for Auroral Research

Case¹,

MacDonald²,

McCloat³

et al. 2016

CSTP

View full text Add to dashboard Cite

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“…The algorithm described by Cheng et al [2] estimates the location of Twitter users based on the text of their tweets. The estimation is entirely content-based and does not rely on meta-data, such as profile or network information.…”

Section: Prediction Of User Traitsmentioning

confidence: 99%

“…The data for the LFM-1b set was acquired by first fetching the overall 250 top tags, 2 to, in turn, gather their top artists. 3 For these artists, the top fans 4 were retrieved, which resulted in 465,000 active users.…”

Section: Datasetmentioning

confidence: 99%

“…It has been shown that many of the digital traces that are left by the users can also be exploited to predict additional information about them such as predicting a person's location from their tweets [2] or predicting personality traits from Facebook Likes [18]. In this work, we focus on digital traces on the social music platform Last.fm, and use various different sources of information either directly from data available via the Last.fm API or extracted from the collected data to infer personal information of users, such as age, gender, and nationality.…”

Section: Introductionmentioning

confidence: 99%

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Predicting user demographics from music listening information

Krismayer

Schedl

Knees

et al. 2018

Multimed Tools Appl

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Online activities such as social networking, online shopping, and consuming multi-media create digital traces, which are often analyzed and used to improve user experience and increase revenue, e. g., through better-fitting recommendations and more targeted marketing. Analyses of digital traces typically aim to find user traits such as age, gender, and nationality to derive common preferences. We investigate to which extent the music listening habits of users of the social music platform Last.fm can be used to predict their age, gender, and nationality. We propose a feature modeling approach building on Term Frequency-Inverse Document Frequency (TF-IDF) for artist listening information and artist tags combined with additionally extracted features. We show that we can substantially outperform a baseline majority voting approach and can compete with existing approaches. Further, regarding prediction accuracy vs. available listening data we show that even one single listening event per user is enough to outperform the baseline in all prediction tasks. We also compare the performance of our algorithm for different user groups and discuss possible prediction errors and how to mitigate them. We conclude that personal information can be derived from music listening information, which indeed can help better tailoring recommendations, as we illustrate with the use case of a music recommender system that can directly utilize the user attributes predicted by our algorithm to increase the quality of it's recommendations.

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“…This latter line of research was made possible by the growing availability of geographic information generated by social media users and GPS-enabled devices. Cheng, et al (2010) used a probabilistic model based on hundreds of tweets to estimate the likelihood of users living in a particular city within a 100-mile radius, while Sakaki, et al (2010) investigated the real-time interaction between onsite events and Twitter stream to monitor tweets and detect events in geographic locations. Noulas, et al (2011) studied urban mobility patterns in several metropolitan areas by analyzing a large set of Foursquare users, and Gao, et al (2012) offered a sociohistorical model to explore user's behavior on location-based social networks.…”

Section: Previous Workmentioning

confidence: 99%

Taking tweets to the streets: A spatial analysis of the Vinegar Protests in Brazil

Bastos

Recuero

Zago

2014

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In this paper we investigate the relationship between the geographic location of protestors attending demonstrations in the 2013 Vinegar protests in Brazil and the geographic location of users that tweeted the protests. We explored the overlap between different sources of geographic information from Twitter -namely geocode, hashtag, and user profileprovided by multiple samples drawn from a population of three million tweets related to the events and compared the data to the location of protestors attending the street demonstrations. We adjusted the data for the uneven distribution of the population and performed geospatial and spatial clustering analysis over sets of spatial locations. We found evidence for the hypotheses that users tweeting the protests are geographically distant from the street protests and that users from geographically isolated areas rely on Twitter hashtags to remotely engage in the demonstrations.

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You are where you tweet

Cited by 803 publications

References 10 publications

Determining the Accuracy of Crowdsourced Tweet Verification for Auroral Research

Determining the Accuracy of Crowdsourced Tweet Verification for Auroral Research

Predicting user demographics from music listening information

Taking tweets to the streets: A spatial analysis of the Vinegar Protests in Brazil

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