Rapid relevance classification of social media posts in disasters and emergencies: A system and evaluation featuring active, incremental and online learning

Kaufhold, Marc–André; Bayer, Markus; Reuter, Christian

doi:10.1016/j.ipm.2019.102132

Cited by 78 publications

(36 citation statements)

References 42 publications

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“…We plan to further complement this research by analyzing tweets from Twitter communities that discuss different topics. In this regard, techniques such as domain adaptation, transfer learning, active and online learning could help models to adapt to new domains and address issues associated with the lack of training data (Johnson et al 2020;Kaufhold et al 2020;Stowe et al 2018).…”

Section: Discussionmentioning

confidence: 99%

Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities

Gruzd

Kumar

Abul-Fottouh

et al. 2020

Comput Supported Coop Work

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As social media become a staple for knowledge discovery and sharing, questions arise about how self-organizing communities manage learning outside the domain of organized, authority-led institutions. Yet examination of such communities is challenged by the quantity of posts and variety of media now used for learning. This paper addresses the challenges of identifying (1) what information, communication, and discursive practices support successful online communities, (2) whether such practices are similar on Twitter and Reddit, and (3) whether machine learning classifiers can be successfully used to analyze larger datasets of learning exchanges. This paper builds on earlier work that used manual coding of learning and exchange in Reddit ‘Ask’ communities to derive a coding schema we refer to as ‘learning in the wild’. This schema of eight categories: explanation with disagreement, agreement, or neutral presentation; socializing with negative, or positive intent; information seeking; providing resources; and comments about forum rules and norms. To compare across media, results from coding Reddit’s AskHistorians are compared to results from coding a sample of #Twitterstorians tweets (n = 594). High agreement between coders affirmed the applicability of the coding schema to this different medium. LIWC lexicon-based text analysis was used to build machine learning classifiers and apply these to code a larger dataset of tweets (n = 69,101). This research shows that the ‘learning in the wild’ coding schema holds across at least two different platforms, and is partially scalable to study larger online learning communities.

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Section: Discussionmentioning

confidence: 99%

Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities

Gruzd

Kumar

Abul-Fottouh

et al. 2020

Comput Supported Coop Work

View full text Add to dashboard Cite

show abstract

“…In the current work, the classical RF algorithm was utilized to model the dynamic nature in both offline training and online update phase using the newly arrived measurements. Instead, using online extensions of RF model in the first place, such as online incremental RF (presented in [35]) or Mondrian forests (described in [36], [37]), may reduce the training and update time. Moreover, there is a number of threats that may have an impact on the results of this study.…”

Section: Conf Matrixmentioning

confidence: 99%

A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation

et al. 2021

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This paper proposes a novel fault detection and diagnosis (FDD) technique for grid-tied PV systems. The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors,...) by using an interval-valued data representation, and with large-scale systems by using a dataset size-reduction framework. The failures encompassed in this study are the open-circuit/short-circuit, islanding, output current sensor, and partial shading faults. In the proposed FDD approach, named interval reduced kernel PCA (IRKPCA)-based Random Forest (IRKPCA-RF), the feature extraction and selection phase is performed using the IRKPCA models while the fault classification is ensured using the RF algorithm. The main contribution of the proposed approach is to provide a good trade-off between low computation time and high classification metrics. The performance of the proposed IRKPCA-RF approach is assessed using a set of emulated data of a grid-tied PV system operating under healthy and faulty conditions. The presented results show that the proposed IRKPCA-RF approach is characterized by enhanced diagnosis metrics, classification rate, and computation time compared to the classical techniques.

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“…[3]. Pada kasus lain, klasifikasi teks membantu mengidentifikasi informasi penting terkait bencana alam [4], menentukan sentimen tentang komentar evaluasi terhadap dosen [5], review film [6] dan berita [7]. Sedangkan teknik klasifikasi teks pada bidang biologi digunakan pada data untaian protein [8] dan DNA [9] [10].…”

Section: Pendahuluanunclassified

Studi Ekstraksi Fitur Berbasis Vektor Word2Vec pada Pembentukan Fitur Berdimensi Rendah

2020

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Klasifikasi teks adalah salah satu metode untuk mengelola dan mencari informasi penting yang terdapat pada format tekstual yang tidak terstruktur. Ekstraksi fitur merupakan proses penting pada klasifikasi teks untuk mengubah format tekstual yang tidak terstruktur menjadi terstruktur sehingga dapat diproses oleh algoritma machine learning untuk mengklasifikasikan ke class yang telah ditentukan. Salah satu teknik ekstraksi fitur yang umum digunakan adalah vector space representation. Teknik ini mudah digunakan tetapi berpotensi menghasilkan data dengan dimensi banyak yang berakibat kepada peningkatan waktu komputasi bahkan tidak dapat diproses karena limitasi perangkat keras. Pada riset ini kami melakukan studi terhadap teknik ekstraksi fitur yang mampu menghasilkan data berdimensi sedikit. Ekstraksi fitur yang digunakan memanfaatkan vektor word2vec untuk mengontrol jumlah fitur yang dihasilkan. Pada riset ini kami membandingkan beberapa model yang dihasilkan sendiri dengan jumlah fitur yang bervariasi dan model yang telah disedikan oleh Google. Hal ini dilakukan untuk mengetahui jumlah fitur yang dapat menghasilkan kinerja klasifikasi terbaik. Hasilnya didapat nilai kinerja tertinggi akurasi yaitu 0.877 dengan jumlah fitur adalah 300 dari model yang dihasilkan sendiri.

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Rapid relevance classification of social media posts in disasters and emergencies: A system and evaluation featuring active, incremental and online learning

Cited by 78 publications

References 42 publications

Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities

Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities

A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation

Studi Ekstraksi Fitur Berbasis Vektor Word2Vec pada Pembentukan Fitur Berdimensi Rendah

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