Understanding Twitter

Maclean, Fiona; Jones, Derek; Carin-Levy, Gail; Hunter, Heather

doi:10.4276/030802213x13706169933021

Cited by 29 publications

(18 citation statements)

References 5 publications

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“…So it was not surprising to find doctors exhorting other doctors to join Twitter [ 3 ]. While voicing concerns with maintaining privacy and professionalism on social media, proponents described largely idyllic scenarios emphasizing Twitter’s potential for aiding career development [ 4 ], connecting with colleagues, building relationships with patients [ 5 ], enabling virtual journal clubs and scientific conferences [ 6 ], complementing traditional teaching methods [ 7 , 8 ], critically appraising and reviewing research [ 9 ], or all of the above [ 6 ]. Some have argued that Twitter promised to speed knowledge transfer and bridge the communication gap between researchers and stakeholders [ 10 ] leading others to suggest strategies and tips to improve the dissemination of research findings [ 11 ], and to engage readers to follow journals’ Twitter accounts [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

The unbearable emptiness of tweeting—About journal articles

et al. 2017

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Enthusiasm for using Twitter as a source of data in the social sciences extends to measuring the impact of research with Twitter data being a key component in the new altmetrics approach. In this paper, we examine tweets containing links to research articles in the field of dentistry to assess the extent to which tweeting about scientific papers signifies engagement with, attention to, or consumption of scientific literature. The main goal is to better comprehend the role Twitter plays in scholarly communication and the potential value of tweet counts as traces of broader engagement with scientific literature. In particular, the pattern of tweeting to the top ten most tweeted scientific dental articles and of tweeting by accounts is examined. The ideal that tweeting about scholarly articles represents curating and informing about state-of-the-art appears not to be realized in practice. We see much presumably human tweeting almost entirely mechanical and devoid of original thought, no evidence of conversation, tweets generated by monomania, duplicate tweeting from many accounts under centralized professional management and tweets generated by bots. Some accounts exemplify the ideal, but they represent less than 10% of tweets. Therefore, any conclusions drawn from twitter data is swamped by the mechanical nature of the bulk of tweeting behavior. In light of these results, we discuss the compatibility of Twitter with the research enterprise as well as some of the financial incentives behind these patterns.

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Section: Introductionmentioning

confidence: 99%

The unbearable emptiness of tweeting—About journal articles

et al. 2017

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“…Pesan public yang dikirim dan diterima melalui twitter atau dikenal dengan 'tweet' dibatasi tidak lebih dari 140 karakter serta dapat menyertakan tautan ke blog, halaman web, gambar, video serta media lainnya. Sebagai alat komunikasi, Twitter memungkinkan pertukaran ide secara bebas secara nasional dan global, antara orang-orang yang tertarik pada bidang keahlian yang serupa, serta memberikan kesempatan untuk terlibat dalam debat kritis [9].…”

Section: Penelitian Terkaitunclassified

Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning

Savitri

Rahman

Venyutzky

et al. 2021

JuTISI

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Covid-19 pandemic urges countries to limit interaction of their people to reduce transmission. Indonesia requires people to do activities at home, one of which is online school. Many people share their thoughts through social media Twitter. Therefore, authors conducted sentiment analysis using supervised machine learning algorithm to determine distribution of words used in commenting on online schools, relationship between sentence, length and sentiment, and best algorithms that can be used to get most accurate results. In this study, authors used the method of crawling with RapidMiner to get data from Twitter. Then authors do data cleansing, data processing with classification methods using Random Forest Classifier , Logistic Regression , BernoulliNB and SVC algorithm. After that authors evaluate using confusion matrix, accuracy rate and classification report. In this research, authors found there are positive, negative, and neutral sentiments expressed on the online school implementation through comments. Authors ranked top three most used words used to express positive sentiments which includes bahagia, rajin and senang. On negative sentiments, top three words are capek, muak and bosen. On neutral sentiments, top three words are tidur, capek, and buka. Lengthy Tweets are usually imbued with negative remarks. On the other hand, the tweet tends to be positive and neutral tweet is usually stable. Authors conclude that the weakness of online school is the amount of workload that makes students tired alongside ineffective teaching method which makes it hard for students to understand the material given by school. However, on the positive side, some people agree with policies that are implemented and they feel like they gained some benefits from the implementation. From the four supervised machine learning algorithms that have been tested, Logistic Regression shows the highest accuracy, 0,87. The analysis shows that society tends to be neutral to the implementation of online school.

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“…Sebelumnya budaya seperti ini juga muncul melalui media sosial Twitter di pertengahan tahun 2000, di mana khalayak secara bebas dapat membentuk jaringan dengan berbagi informasi terkini (Mistry, 2011). Walaupun dibatasi oleh jumlah karakter tertentu, tidak menutup kreativitas para penggunanya mengingat Twitter juga dapat diberi tautan ke situs lain, diberi unggahan foto hingga video (Maclean, et al, 2013).…”

Section: Pendahuluanunclassified

Peran Aktivis Media Sosial (Buzzer) Sukarela: Studi Kasus Member Independen Herbalife Nutrition

Akmal¹,

Irwansyah²

2020

CALATHU

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Kehadiran buzzer di era media sosial yang tumbuh pesat banyak dimanfaatkan oleh perusahaan atau pihak-pihak tertentu karena memiliki pengaruh dalam menyampaikan pesan kepada khalayak, yang tentu saja mendatangkan kembali keuntungan bagi buzzer sebagai sumber pemasukan. Dalam media sosial, meski banyak buzzer yang memiliki motif ekonomi, namun ada pula buzzer yang menggunakan media sosial untuk menunggah hal-hal yang disukai secara sukarela tanpa imbalan. Hal ini terlihat jelas pada Herbalife Nutrition, sebuah direct selling company yang umumnya keberadaan distributor atau member (anggota) hanya berorientasi pada keuntungan penjualan produk dan mengembangkan downline. Penelitian ini bertujuan untuk menganalisis dan mengungkap buzzer sukarela yang menjadi garda terdepan sebuah brand atau perusahaan. Penelitian ini menggunakan pendekatan deskriptif kualitatif yang mengungkap kenyataan atau fakta, keadaan, fenomena, variabel serta keadaan yang ada. Hasilnya, member independen Herbalife Nutrition yang mencintai produk dan menyukai gaya hidup sehat aktif sesuai dengan filosofi perusahaan, kemudian merasa memiliki ikatan dan menjadi buzzer sukarela yang berperan sebagai penjaga citra perusahaan atau brand. Hal ini tentu mengangkat sisi baru atas kegiatan buzzer di media sosial. Kata kunci: buzzer sukarela, multi-level marketing; Herbalife Nutrition

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Understanding Twitter

Cited by 29 publications

References 5 publications

The unbearable emptiness of tweeting—About journal articles

The unbearable emptiness of tweeting—About journal articles

Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning

Peran Aktivis Media Sosial (Buzzer) Sukarela: Studi Kasus Member Independen Herbalife Nutrition

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