Unlocking Social Media and User Generated Content as a Data Source for Knowledge Management

Meneghello, James; Thompson, Nik; Lee, Kevin; Wong, Kok Wai; Abu-Salih, Bilal

doi:10.4018/ijkm.2020010105

Cited by 36 publications

(22 citation statements)

References 49 publications

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“…In OSNs, determining users' influence in an unambiguous domain has been driven by its significance in an extensive range of applications such as personalized recommendation systems [105], opinion analysis [106], expertise retrieval [107], and computational advertising [108]. Sentiment analysis has indeed become a core pillar of researchers' endeavours to create applications that are influenced by the massive increase of User Generated Content (UGC) [110,111]. For example, UGC in OSNs has been examined to study their effective data extracted and applied to numerous applications [112][113][114].…”

Section: Discussionmentioning

confidence: 99%

Time-aware domain-based social influence prediction

et al. 2020

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Online Social Networks(OSNs) have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, the concept of social trust has attracted an attention of information processors/data scientists and information consumers / business firms. One of the main reasons for acquiring the value of Social Big Data (SBD) is to provide frameworks and methodologies using which the credibility of OSNs users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring credibility of SBD. Given the exposed environment's settings and fewer limitations related to OSNs, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. Hence, this paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

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Section: Discussionmentioning

confidence: 99%

Time-aware domain-based social influence prediction

et al. 2020

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“…Ontology Interoperability :The interlinking with other relevant entities defined in other datasets supports interoperability [45,46,70,71]. The approach taken in this study addresses information interoperability by focusing on the equivalence links that direct the URI to refer to the same resource or entity.…”

Section: Political Domain Knowledge Inferencementioning

confidence: 99%

Predictive Analytics Using Social Big Data and Machine Learning

Abu-Salih

Wongthongtham

Zhu

et al. 2021

Social Big Data Analytics

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The ever-increase in the quality and quantity of data generated from day-to-day businesses operations in conjunction with the continuously imported related social data have made the traditional statistical approaches inadequate to tackle such data floods. This has dictated researchers to design and develop advance and sophisticated analytics that can be incorporated to gain valuable insights that benefit business domain. This chapter sheds the light on core aspects that lay the foundations for social big data analytics. In particular, the significant of predictive analytics in the context of SBD is discussed fortified with presenting a framework for SBD predictive analytics. Then, various predictive analytical algorithms are introduced with their usage in several important application and top-tier tools and APIs. A case study on using predictive analytics to social data is provided supported with experiments to substantiate significance and utility of predictive analytics.

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“…19 Media tidak lagi hanya untuk berkomunikasi, tetapi juga berbagi informasi dan pengetahuan. 20 Jaringan media sosial bisa dibangun melalui kelompok diskusi maya (newsgroup), tulisan di internet (blog-web), video interaktif (video on demand), dan siaran audio (podcasting). 21 Demikanlah media komunikasi berkembang dengan pesatnya.…”

Section: Pendahuluanunclassified

Media Sosial Sebagai Sarana Katakese Mahasiswa di Malang, Jawa Timur

Derung¹

2021

KENOSIS: Jurnal Kajian Teologi

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Penelitian ini dilakukan untuk mengungkapkan bagaimana penggunaan media sosial untuk kegiatan katekese oleh para mahasiswa di Kota Malang, Jawa Timur. Penelitian ini menggunakan metode kuantitatif. Subjek penelitian adalah mahasiswa sekolah tinggi agama di Malang, Jawa Timur sebanyak 61 orang. Pengumpulan data menggunakan angket tertutup dengan menggunakan Google Form. Teknik analisis data menggunakan scoring, F prosen dan uji binomial. Pengolahan data menggunakan rumus scoring, memperoleh score rata-rata 1,72. Artinya dalam menggunakan media sosial sebagai sarana katekese, mahasiswa menjalankan keterlibatannya dengan baik. Berdasarkan hasil pengolahan data menggunakan uji binomial diperoleh nilai z = 4,05. Jika dibandingkan dengan nilai tabel maka nilai tersebut lebih rendah dari taraf signifikan 0,05 artinya ada perbedaan antara keterlibatan mahasiswa yang satu dengan yang lain dalam menggunakan media sosial sebagai sarana katekese.AbstractThis research was conducted to reveal how the use of social media for catechesis activities by the students in Malang City, East Java. This research uses quantitative methods. Research subjects are students of religious high schools in Malang, East Java, as many as 61 people. Data collection using a closed questionnaire using Google Form. The data analysis technique used scoring, F percent and binomial test. Processing data using the scoring formula, obtaining an average score of 1.72. This means that in using social media as a means of catechesis, students carry out their involvement well. Based on the results of data processing using the binomial test, the value of z = 4.05 was obtained. When compared with the table value, this value is lower than the significant level of 0.05, meaning that there is a difference between the involvement of one student and another in using social media as a means of catechesis

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Unlocking Social Media and User Generated Content as a Data Source for Knowledge Management

Cited by 36 publications

References 49 publications

Time-aware domain-based social influence prediction

Time-aware domain-based social influence prediction

Predictive Analytics Using Social Big Data and Machine Learning

Media Sosial Sebagai Sarana Katakese Mahasiswa di Malang, Jawa Timur

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