The where in the tweet

Li, Wen; Serdyukov, Pavel; Vries, Arjen P. de; Eickhoff, Carsten; Larson, Martha

doi:10.1145/2063576.2063995

Cited by 77 publications

(65 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They include classifying Twitter users (Pennacchiotti & Popescu, 2011), or describing geotemporal demographics (Longley, Adnan, & Lansley, 2015). Furthermore, the analysis of textual content to extract spatial information is becoming increasingly important (Cheng, Caverlee, & Lee, 2010;Dalvi, Kumar, & Pang, 2012;Kinsella, Murdock, & Hare, 2011;Li, Serdyukov, de Vries, Eickhoff, & Larson, 2011), including the generation of ambient geographic information (Stefanidis, Crooks, & Radzikowski, 2013), or the definition of geotag gazetteers (Keßler, Maué, Heuer, & Bartoschek, 2009). …”

Section: Citizen-contributed Geographic Information To Describe Urbanmentioning

confidence: 99%

#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

Kovács-Györi

Ristea

Havas

et al. 2018

View full text Add to dashboard Cite

The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because their characteristics often contradict daily planning routines. Due to the availability of large amounts of data, social media offer the possibility for fine-scale spatial and temporal analysis in this context, especially regarding public emotions related to varied topics. Thus, this article proposes a combined approach for analyzing large sports events considering event days vs comparison days (before or after the event) and different user groups (residents vs visitors), as well as integrating sentiment analysis and topic extraction. Our results based on various analyses of tweets demonstrate that different spatial and temporal patterns can be identified, clearly distinguishing both residents and visitors, along with positive or negative sentiment. Furthermore, we could assign tweets to specific urban events or extract topics related to the transportation infrastructure. Although the results are potentially able to support urban planning processes of large events, the approach still shows some limitations including well-known biases in social media or shortcomings in identifying the user groups and in the topic modeling approach.

show abstract

Section: Citizen-contributed Geographic Information To Describe Urbanmentioning

confidence: 99%

#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

Kovács-Györi

Ristea

Havas

et al. 2018

View full text Add to dashboard Cite

show abstract

“…This would be quite useful where there is limited and sparse location information within the body of the message text. [8] addresses the sparsity problem of tweets in locating points of interests by employing webpage snippets. Rae, [26] searches Wikipedia to get structured information about places to complement tweets about points of interests (PoIs).…”

Section: Time Zonesmentioning

confidence: 99%

“…Various works have employed diverse kinds of spatial features to infer the location of online users including use of metadata information such as time of post [8]. Some have used only the content of the tweets [9][10] [11].…”

Section: Introductionmentioning

confidence: 99%

A survey of location inference techniques on Twitter

Ajao

Hong

Liu

2015

Journal of Information Science

132

View full text Add to dashboard Cite

The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or author's location remains a challenge thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state-of-the-art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features.

show abstract

“…Several researchers have investigated the problem of geolocation detection from tweet content [1,2,3,4]. Cheng et al [1] tackle this problem in the city level.…”

Section: Related Workmentioning

confidence: 99%

“…They also employ an unsupervised measurements to rank the local words which remove the noises effectively. The authors in [2] are instead interested in the place of interest (POI) that a tweet refers to. The authors formalize the problem by ranking a set of candidate POIs using language and time models.…”

Section: Related Workmentioning

confidence: 99%

Location Type Classification Using Tweet Content

Liu

Luo

Lee

2012

2012 11th International Conference on Machine Learning and Applications

View full text Add to dashboard Cite

Abstract-Location context in social media plays an important role in many applications. In addition to explicit location sharing via popular "check in" service, user-posted content could also implicitly reveals users' location context. Identifying such a location context based on content is an interesting problem because it is not only important in inferring social ties between people, but also vital for applications such as user profiling and targeted advertising. In this paper, we study the problem of location type classification using tweet content. We extend probabilistic text classification models to incorporate temporal features and user history information in terms of probabilistic priors. Experimental results show that our extensions can boost classification accuracy effectively.

show abstract

The where in the tweet

Cited by 77 publications

References 7 publications

#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

A survey of location inference techniques on Twitter

Location Type Classification Using Tweet Content

Contact Info

Product

Resources

About