Fine-Scale Prediction of People’s Home Location Using Social Media Footprints

Kavak, Hamdi; Vernon-Bido, Daniele; Padilla, José J.

doi:10.1007/978-3-319-93372-6_20

Cited by 15 publications

(15 citation statements)

References 6 publications

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“…With an increasing variety of social media platforms and easily accessible information posted directly by people about their daily lives, key events, and their likes and dislikes, there are growing possibilities for connecting simulations directly into the "human" component of data. Kavak, Vernon-Bido [31] explore the use of social media data in simulations as sources of input data, for calibration, for recognizing mobility patterns, and for identifying communication patterns. Padilla, Kavak [32] use tweets to identify individual-level tourist visit patterns and sentiment.…”

Section: Plos Onementioning

confidence: 99%

“…The challenges of differentiating errors from unexpected behaviors and in tracing events, interactions, and outcomes back to the underlying model specifications will continue to increase when dealing with systems of dynamic structure [26][27][28], human behaviors [29,30], initialization using unstructured data [31,32], and network homophily across multiple dimensions [33,34]. Additionally, new challenges may emerge from continued methodological and technological advances, such as dynamically allocating computational loads at runtime [35], building and running simulations on the web [36][37][38][39], constructing hybrid simulations [40][41][42], and endeavoring to lower the barrier of entry for M&S to encourage STEM research [37,39,43].…”

Section: Introductionmentioning

confidence: 99%

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A content analysis-based approach to explore simulation verification and identify its current challenges

et al. 2020

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Verification is a crucial process to facilitate the identification and removal of errors within simulations. This study explores semantic changes to the concept of simulation verification over the past six decades using a data-supported, automated content analysis approach. We collect and utilize a corpus of 4,047 peer-reviewed Modeling and Simulation (M&S) publications dealing with a wide range of studies of simulation verification from 1963 to 2015. We group the selected papers by decade of publication to provide insights and explore the corpus from four perspectives: (i) the positioning of prominent concepts across the corpus as a whole; (ii) a comparison of the prominence of verification, validation, and Verification and Validation (V&V) as separate concepts; (iii) the positioning of the concepts specifically associated with verification; and (iv) an evaluation of verification's defining characteristics within each decade. Our analysis reveals unique characterizations of verification in each decade. The insights gathered helped to identify and discuss three categories of verification challenges as avenues of future research, awareness, and understanding for researchers, students, and practitioners. These categories include conveying confidence and maintaining ease of use; techniques' coverage abilities for handling increasing simulation complexities; and new ways to provide error feedback to model users.

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Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A content analysis-based approach to explore simulation verification and identify its current challenges

et al. 2020

Self Cite

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“…We used an identical dataset and test procedure as [9] in order to ensure a fair comparison. We used 5-fold crossvalidation in both phases of our model identical as state of the art, which means that the model is trained using 80% of the dataset and validated using the remaining 20% in each of five experiments.…”

Section: B Experimental Setupmentioning

confidence: 99%

High-resolution home location prediction from tweets using deep learning with dynamic structure

Ghaffari

Srinivasan

Liu

2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Timely and high-resolution estimates of the home locations of a sufficiently large subset of the population are critical for effective disaster response and public health intervention, but this is still an open problem. Conventional data sources, such as census and surveys, have a substantial time lag and cannot capture seasonal trends. Recently, social media data has been exploited to address this problem by leveraging its large user-base and real-time nature. However, inherent sparsity and noise, along with large estimation uncertainty in home locations, have limited their effectiveness. Consequently, much of previous research has aimed only at a coarse spatial resolution, with accuracy being limited for high-resolution methods. In this paper, we develop a deep-learning solution that uses a two-phase dynamic structure to deal with sparse and noisy social media data. In the first phase, high recall is achieved using a random forest, producing more balanced home location candidates. Then two deep neural networks are used to detect home locations with high accuracy. We obtained over 90% accuracy for large subsets on a commonly used dataset. Compared to other high-resolution methods, our approach yields up to 60% error reduction by reducing high-resolution home prediction error from over 21% to less than 8%. Systematic comparisons show that our method gives the highest accuracy both for the entire sample and for subsets. Evaluation on a real-world public health problem further validates the effectiveness of our approach.

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“…Geo-social media data have been used to infer observable aspects of geo-social media users, especially in the movement and location of users. The location of the home o f geo-social media users can be inferred by using the location shared in tweets, with an accuracy rate of 80% from a sample rate of as low as 1.5 tweets per day [9]. Another study focused on the movement of users, which was derived with 95% accuracy rate using data collected over a 12 month period [10].…”

Section: Introductionmentioning

confidence: 99%

Fusing location and text features for sentiment classification

Lim¹,

Ho²,

Ting³

2020

IJETT

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Geo-tagged Twitter data has been used recently to infer insights on the human aspects of social media. Insights related to demographics, spatial distribution of cultural activities, space-time travel trajectories for humans as well as happiness has been mined from geo-tagged twitter data in recent studies. To date, not much study has been done on the impact of the geo-location features of a Tweet on its sentiment. This observation has inspired us to propose the usage of geo-location features as a method to perform sentiment classification. In this method, the sentiment classification of geo-tagged tweets is performed by concatenating geo-location features and one-hot encoded word vectors as inputs for convolutional neural networks (CNN) and long short-term memory (LSTM) networks. The addition of language-independent features in the form of geo-location features has helped to enrich the tweet representation in order to combat the sparse nature of short tweet message. The results achieved has demonstrated that concatenating geo-location features to one-hot encoded word vectors can achieve higher accuracy as compared to the usage of word vectors alone for the purpose of sentiment classification.

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Fine-Scale Prediction of People’s Home Location Using Social Media Footprints

Cited by 15 publications

References 6 publications

A content analysis-based approach to explore simulation verification and identify its current challenges

A content analysis-based approach to explore simulation verification and identify its current challenges

High-resolution home location prediction from tweets using deep learning with dynamic structure

Fusing location and text features for sentiment classification

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