Evidential estimation of event locations in microblogs using the Dempster–Shafer theory

Özdikiş, Özer; Oğuztüzün, Halit; Karagöz, Pınar

doi:10.1016/j.ipm.2016.06.001

Cited by 23 publications

(14 citation statements)

References 27 publications

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“…Moreover, few users use geotagging (e.g., due to privacy issues) (Schulz et al, 2013), which reduces greatly the impact of supervised learning WD methods. Some recent studies adopt hybrid approaches, combining: WD and FN (e.g, (Rahimi et al, 2015;Bakerman et al, 2018)); WD and features derived from user account Metadata (MD) (Ozdikis et al, 2016;Dredze et al, 2013;Schulz et al, 2013); and WD, FN and MD (Williams et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

“…In contrast with several state-of-the-art works (e.g., Celik & Dokuz, 2018;Do et al, 2018;Huang & Carley, 2019;Paule et al, 2019;Ozdikis et al, 2019), a pure unsupervised WD approach is adopted and thus no geographic labeled data (e.g., tweets or user location profiles) is required, only historical tweet nouns and GT data. Moreover, we do not use LIW, as adopted in (Ozdikis et al, 2016;Williams et al, 2017;Bakerman et al, 2018;Shahraki et al, 2019), since LIW often assumes finite and rather static set of locations, typically associated to small world regions. Instead, we use tweet nouns, which can be dynamically updated and that can refer to geographic words and also other terms with a location context (e.g., events, people or organizations).…”

Section: Most Research Work That Focus On Geographic Coordinates (Gcmentioning

confidence: 99%

“…Spatial data is a core element of several Web and social media analytics. In effect, knowing the country, city or even more fine-grained location of a user can help in: event detection, disaster early warnings, road traffic prediction, public welfare activity information and tourism prediction (Ozdikis et al, 2016;Zahra et al, 2020;Alkouz & Aghbari, 2020;Chen et al, 2020;Khatibi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Google Trends spatial clustering approach for a worldwide Twitter user geolocation

Zola

Ragno²,

Cortez

2020

Information Processing & Management

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User location data is valuable for diverse social media analytics. In this paper, we address the non-trivial task of estimating a worldwide city-level Twitter user location considering only historical tweets. We propose a purely unsupervised approach (no location data is used) that is based on a synthetic geographic sampling of Google Trends (GT) city-level frequencies of tweet nouns and three clustering algorithms. The approach was validated empirically by using a recently collected dataset, with 3,268 worldwide city-level locations of Twitter users, obtaining competitive results when compared with a state-of-the-art Word Distribution (WD) user location estimation method. The best overall results were achieved by the GT noun (GTN) DBSCAN (GTN-DB) method, which is computationally fast, and correctly predicts the ground truth locations of 15%, 23%, 39% and 58% of the users for tolerance distances of 250 km, 500 km, 1,000 km and 2,000 km.

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Section: Related Workmentioning

confidence: 99%

Section: Most Research Work That Focus On Geographic Coordinates (Gcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Google Trends spatial clustering approach for a worldwide Twitter user geolocation

Zola

Ragno²,

Cortez

2020

Information Processing & Management

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“…The location mostly referred to in users' profiles is assigned as the event location. The implementation of this method is inspired to the work of Giridhar et al [35] and it is already used as baseline in [36]; • Maximum Location Frequency (LocFreq): this approach consists of finding the frequencies of locations specified by the users when they posted a tweet about an event, namely the frequencies of the values of the attribute place.name. The location value mostly specified is assigned as the event location.…”

Section: Comparison With Literature Approachesmentioning

confidence: 99%

Automatic Event Geo-Location in Twitter

et al. 2020

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Twitter is currently one of the most popular platforms for disseminating information about events happening around the world. Especially but not only for emergency events, it is crucial to know when and where the events are taking place. Unfortunately, identifying the geo-location of an event discussed in Twitter is a very challenging task mainly due to the brevity of the messages (i.e., tweets) and their subjective nature. In the literature, some efforts have been made to address this task, but they are characterized by substantial limitations such as the use of exclusively text analysis techniques, or the need for keywords or possible candidate locations. This paper proposes a new process for automatic event geo-localization which relies on both textual and spatial/temporal use of content posted on Twitter without using some prior knowledge about the event to be located. As shown by experimental results, our proposal achieves a good accuracy rate and outperforms two well-known baseline approaches related to the geo-location of events in Twitter.

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“…These platforms, also called as online social networks (OSNs), have become part of the daily life of millions of people around the world who constantly maintain and create new social relationships [2,3]. OSNs providing location-based services for users to check-in in a physical place are called location-based social networks (LBSNs) [4,5,6,7].…”

Section: Introductionmentioning

confidence: 99%

The role of location and social strength for friendship prediction in location-based social networks

Valverde-Rebaza

Roche

Poncelet

et al. 2018

Information Processing & Management

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Recent advances in data mining and machine learning techniques are focused on exploiting location data. There, combined with the increased availability of location-acquisition technology, has encouraged social networking services to offer to their users different ways to share their location information. These social networks, called location-based social networks (LBSNs), have attracted millions of users and the attention of the research community. One fundamental task in the LBSN context is the friendship prediction due to its role in different applications such as recommendation systems. In the literature exists a variety of friendship prediction methods for LBSNs, but most of them give more importance to the location information of users and disregard the strength of relationships existing between these users. The contributions of this article are threefold, we: 1) carried out a comprehensive survey of methods for friendship prediction in LBSNs and proposed a taxonomy to organize the existing methods; 2) put forward a proposal of five new methods addressing gaps identified in our survey while striving to find a balance between optimizing computational resources and improving the predictive power; and 3) used a comprehensive evaluation to quantify the prediction abilities of ten current methods and our five proposals and selected the top-5 friendship prediction methods for LBSNs. We thus present a general panorama of friendship prediction task in the LBSN domain with balanced depth so as to facilitate research and real-world application design regarding this important issue.

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Evidential estimation of event locations in microblogs using the Dempster–Shafer theory

Cited by 23 publications

References 27 publications

A Google Trends spatial clustering approach for a worldwide Twitter user geolocation

A Google Trends spatial clustering approach for a worldwide Twitter user geolocation

Automatic Event Geo-Location in Twitter

The role of location and social strength for friendship prediction in location-based social networks

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