Twitter User Geolocation Using Deep Multiview Learning

Huu, Tien; Nguyen, Duc Minh; Tsiligianni, Evaggelia; Cornelis, Bruno; Deligiannis, Nikos

doi:10.1109/icassp.2018.8462191

Cited by 28 publications

(9 citation statements)

References 25 publications

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“…The first one uses textual contents from tweets, and the latter uses the connections and interactions between users. In Do et al (2018), a neural network model based on multiview learning by combining knowledge from both user-generated content and network interaction is proposed to infer users' locations.…”

Section: Online Social Networkmentioning

confidence: 99%

Containing Future Epidemics With Trustworthy Federated Systems for Ubiquitous Warning and Response

Carrillo¹,

Nguyen²,

Nardelli³

et al. 2021

Front. Comms. Net.

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In this paper, we propose a global digital platform to avoid and combat epidemics by providing relevant real-time information to support selective lockdowns. It leverages the pervasiveness of wireless connectivity while being trustworthy and secure. The proposed system is conceptualized to be decentralized yet federated, based on ubiquitous public systems and active citizen participation. Its foundations lie on the principle of informational self-determination. We argue that only in this way it can become a trustworthy and legitimate public good infrastructure for citizens by balancing the asymmetry of the different hierarchical levels within the federated organization while providing highly effective detection and guiding mitigation measures toward graceful lockdown of the society. To exemplify the proposed system, we choose a remote patient monitoring as use case. This use case is evaluated considering different numbers of endorsed peers on a solution that is based on the integration of distributed ledger technologies and NB-IoT (narrowband IoT). An experimental setup is used to evaluate the performance of this integration, in which the end-to-end latency is slightly increased when a new endorsed element is added. However, the system reliability, privacy, and interoperability are guaranteed. In this sense, we expect active participation of empowered citizens to supplement the more usual top-down management of epidemics.

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Section: Online Social Networkmentioning

confidence: 99%

Containing Future Epidemics With Trustworthy Federated Systems for Ubiquitous Warning and Response

Carrillo¹,

Nguyen²,

Nardelli³

et al. 2021

Front. Comms. Net.

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“…This work motivates us to study further neural network-based models and to evaluate their potential contribution to resolve the geolocation task. In contrast to recent approaches (Miura et al 2016;Lau et al 2017;Elaraby and Abdul-Mageed 2018;Ebrahimi et al 2018;Do et al 2018),…”

Section: Twitterer Geolocationmentioning

confidence: 82%

Word embeddings and deep learning for location prediction: tracking Coronavirus from British and American tweets

Hasni

Faïz

2021

Soc. Netw. Anal. Min.

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With the propagation of the Coronavirus pandemic, current trends on determining its individual and societal impacts become increasingly important. Recent researches grant special attention to the Coronavirus social networks infodemic to study such impacts. For this aim, we think that applying a geolocation process is crucial before proceeding to the infodemic management. In fact, the spread of reported events and actualities on social networks makes the identification of infected areas or locations of the information owners more challenging especially at a state level. In this paper, we focus on linguistic features to encode regional variations from short and noisy texts such as tweets to track this disease. We pay particular attention to contextual information for a better encoding of these features. We refer to some neural network-based models to capture relationships between words according to their contexts. Being examples of these models, we evaluate some word embedding ones to determine the most effective features’ combination that has more spatial evidence. Then, we ensure a sequential modeling of words for a better understanding of contextual information using recurrent neural networks. Without defining restricted sets of local words in relation to the Coronavirus disease, our framework called DeepGeoloc demonstrates its ability to geolocate both tweets and twitterers. It also makes it possible to capture geosemantics of nonlocal words and to delimit the sparse use of local ones particularly in retweets and reported events. Compared to some baselines, DeepGeoloc achieved competitive results. It also proves its scalability to handle large amounts of data and to geolocate new tweets even those describing new topics in relation to this disease.

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“…However, recent multi-modal machine learning work has shown the benefits of late fusion mechanisms (Ramachandram and Taylor, 2017). Do et al (2017) argument in favor of concatenating the hidden layers instead of concatenating the features at input time. Such multi-modal models have been successfully applied in other areas, mostly combining inputs across different domains, for instance, learning speech reconstruction from silent videos (Ephrat et al, 2017), or for text classification using images (Kiela et al, 2018).…”

Section: Multi-modal Machine Learning Architecturementioning

confidence: 99%

Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

Hollenstein

Renggli

Glaus

et al. 2021

Front. Hum. Neurosci.

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Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can also be beneficial in machine learning-based natural language processing tasks. Using EEG brain activity for this purpose is largely unexplored as of yet. In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial. We present a multi-modal machine learning architecture that learns jointly from textual input as well as from EEG features. We find that filtering the EEG signals into frequency bands is more beneficial than using the broadband signal. Moreover, for a range of word embedding types, EEG data improves binary and ternary sentiment classification and outperforms multiple baselines. For more complex tasks such as relation detection, only the contextualized BERT embeddings outperform the baselines in our experiments, which raises the need for further research. Finally, EEG data shows to be particularly promising when limited training data is available.

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Twitter User Geolocation Using Deep Multiview Learning

Cited by 28 publications

References 25 publications

Containing Future Epidemics With Trustworthy Federated Systems for Ubiquitous Warning and Response

Containing Future Epidemics With Trustworthy Federated Systems for Ubiquitous Warning and Response

Word embeddings and deep learning for location prediction: tracking Coronavirus from British and American tweets

Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

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