scholarly journals Topic Modeling Analysis of Social Enterprises: Twitter Evidence

2020 ◽  
Vol 12 (8) ◽  
pp. 3419 ◽  
Author(s):  
Shr-Wei Kao ◽  
Pin Luarn
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Application Programming Interface ◽  
Social Enterprises ◽  
Four Dimensions ◽  
Modeling Analysis ◽  
Application Programming ◽  
Rich Information ◽  
Programming Interface

Social media is a major channel used for communication by professional and social groups. The text posted on social media contains extremely rich information. To capture the development of social enterprises (SEs), this paper examines the tweets posted on Twitter and searches the hashtags on the Twitter Application Programming Interface (API) that SEs deem to be the most important. The results suggest that these tweets can be divided into three content groups (strategy, impact and business). This paper expands this into four dimensions (strategy, impact, business and people) and six indicators (social, opportunity, change, enterprise, network and team) and establishes a conceptual framework of SEs. This paper aims to enhance the understanding of the pertinent issues recently affecting SEs and extract findings that can act as a reference for follow-up studies.

Tracking via Geotagged Social Media Data

2020 ◽  
Author(s):  
Anne Hardy
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Single Point ◽  
Application Programming Interface ◽  
Continuous Location ◽  
Social Media Data ◽  
Large Numbers ◽  
Application Programming ◽  
Media Data ◽  
Programming Interface

Over the past twenty years, social media has changed the ways in which we plan, travel and reflect on our travels. Tourists use social media while travelling to stay in touch with friends and family, enhance their social status (Guo et al., 2015); and assist others with decision making (Xiang and Gretzel, 2010; Yoo and Gretzel, 2010). They also use it to report back to their friends and family where they are. This can be done using a geotag function that provides a location for where a post is made. While little is known about why tourists choose to geotag their social media posts, Chung and Lee (2016) suggest that geotags may be used in an altruistic manner by tourists, in order to provide information, and because they elicit a sense of anticipated reward. What is known, however, is that the function offers researchers the ability to understand where tourists travel. There are two types of geotagged social media data. The first of these is discussed in this chapter and may be defined as single point geo-referenced data – geotagged social media posts whose release is chosen by the user. This includes data gathered from social media apps such as Facebook, Instagram, Twitter and WeiChat. The method of obtaining this data involves the collation of large numbers of discrete geotagged updates or photographs. Data can be collated via an application programming interface (API) provided by the app developer to researchers, by automated data scraping via computer programs, perhaps written in Python, or manually by researchers. The second type of data is continuous location-based data from applications that are designed to track movement constantly, such as Strava or MyFitnessPal. Tracking methods using this continuous location-based data are discussed in detail in the following chapter.

scholarly journals Implementasi Metode Klasifikasi Naive Bayes Pada Sistem Analisis Opini Pengguna Twitter Berbasis Web

2021 ◽  
Vol 10 (1) ◽  
pp. 46-54
Author(s):  
Apif Supriadi ◽  
Fatmasari
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Application Programming Interface ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Public Sentiment ◽  
Application Programming ◽  
Analysis System ◽  
Programming Interface

Abstract— Development of social media which is the result of technological development is an inseparable part of people's lives. Social media is a place where ordinary people express their feelings and opinions about something that concerns them. Inknowing the direction of public sentiment, surveys are usually done online or offline, this sentiment analysis system will facilitate and speed up the process of knowing the direction of public sentiment, in the case of research. This uses data from Twitter social media called tweets or tweets, web-based sentiment analysis system that will classify tweets into 3 (three) types of sentiments, namely positive, neutral and negative, then make a percentage to make it easier to see the direction of public sentiment. In classifying this system uses the Naive Bayes Classifier method and displays it in a web interface with the PHP programming language and uses the Application Programming Interface (API) to get data from Twitter. Intisari — Saat ini perkembangan media sosial yang merupakan hasil dari perkembangan teknologi menjadi bagian tak terpisahkan dari kehidupan masyarakat. Media sosial menjadi tempat masyarakat biasa mengutarakan berbagai perasaan dan opininya tentang suatu hal yang jadi perhatian mereka, dalam mengetahui arah sentimen masyarakat biasanya dilakukan survei baik secara online atau offline, sistem analisis sentimen ini akan memudahkan dan mempercepat proses mengetahui arah sentimen publik, dalam kasus penelitian ini menggunakan data dari media sosial Twitter yang disebut dengan tweets atau cuitan, sistem analisis sentimen berbasis web yang akan mengklasifikasikan cuitan kedalam 3 (tiga) jenis sentimen yaitu positif, netral dan negatif lalu melakukan persentasenya agar mempermudah melihat arah sentimen publik. Dalam melakukan klasifikasinya sistem ini menggunakan metode Naive Bayes Classifier dan menampilkannya dalam antarmuka web dengan bahasa pemrograman PHP dan menggunakan Application Programming Interface (API) dalam mendapatkan data dari Twitter.

scholarly journals Sistem Pengukur Curah Hujan sebagai Deteksi Dini Kekeringan pada Pertanian Berbasis Internet of Things (IoT)

2020 ◽  
Vol 20 (02) ◽  
pp. 131-135
Author(s):  
Satrio Setyo Laksono ◽  
N Nurgiyatna
Keyword(s):  
Social Media ◽  
Internet Of Things ◽  
Application Programming Interface ◽  
Raspberry Pi ◽  
Application Programming ◽  
Programming Interface

Hujan merupakan faktor penting dalam segala aspek kehidupan, tidak tentunya kedatangan hujan saat ini sangat berpengaruh terhadap kegiatan industri, terutama pada sektor pertanian. Dalam industri pertanian tinggi rendahnya curah hujan sangat berpengaruh dalam pengelolaan sumber daya dan juga sangat menentukan hasil panen yang akan didapati. Sebagai contoh curah hujan yang terlalu tinggi dapat menyebabkan gagal panen dikarenakan resiko terjadinya banjir yang membuat tanaman padi mati, begitu pula curah hujan yang terlalu sedikit dapat pula menyebabkan gagal panen dikarenakan kurangnya sumber air. Dengan adanya Inernet of Things (IoT) dan Social Media diharapkan dapat membantu para petani dalam mengelola sumber air untuk lahan pertanian mereka. Penelitian ini menggunakan metode eskperimental dengan percobaan langsung terhadap hujan. Raspberry pi 3 digunakan sebagai controller, sensor magnetik diterapkan sebagai pengukur curah hujan yang dihubungkan memalui interface GPIO pada raspberry pi 3, dan diteruskan pada API (Application Programming Interface) Telegram berupa bot sebagai sebuah pesan. Tampilan pengontrol berupa sebuah layar chat yang terdapat pada telegram yang dapat digunakan untuk mengirim perintah yang sudah disediakan.

Correlating Self-Report and Trace Data Measures of Incivility: A Proof of Concept

2018 ◽  
Vol 38 (5) ◽  
pp. 584-599 ◽  
Author(s):  
Toby Hopp ◽  
Chris J. Vargo ◽  
Lucas Dixon ◽  
Nithum Thain
Keyword(s):  
Social Media ◽  
Political Communication ◽  
Application Programming Interface ◽  
Self Report ◽  
Proof Of Concept ◽  
Online Behavior ◽  
Communication Behaviors ◽  
Trace Data ◽  
Application Programming ◽  
Programming Interface

This study correlated self-report and trace data measures of political incivility. Specifically, we asked respondents to provide estimates of the degree to which they engage in uncivil political communication online. These estimates were then compared to computational measures of uncivil social media discussion behavior. The results indicated that those who self-disclose uncivil online behavior also tend to generate content on social media that is uncivil as identified by Google’s Perspective application programming interface. Taken as a whole, this work suggests that combining self-report and behavioral trace data may be a fruitful means of developing multimethod measures of complex communication behaviors.

Social Media as Mirror of Society

2017 ◽  
pp. 128-141
Author(s):  
Amir Manzoor
Keyword(s):  
Social Media ◽  
Application Programming Interface ◽  
Social Media Data ◽  
Social Media Platform ◽  
Application Programming ◽  
Media Platform ◽  
Use Of Social Media ◽  
Media Data ◽  
Programming Interface ◽  
Specific Reason

Over the last decade, social media use has gained much attention of scholarly researchers. One specific reason of this interest is the use of social media for communication; a trend that is gaining tremendous popularity. Every social media platform has developed its own set of application programming interface (API). Through these APIs, the data available on a particular social media platform can be accessed. However, the data available is limited and it is difficult to ascertain the possible conclusions that can be drawn about society on the basis of this data. This chapter explores the ways social researchers and scientists can use social media data to support their research and analysis.

scholarly journals Facilitating API lookup for novices learning data wrangling using thumbnail graphics

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Lovisa Sundin ◽  
Nourhan Sakr ◽  
Juho Leinonen ◽  
Quintin Cutts
Keyword(s):  
Data Science ◽  
Application Programming Interface ◽  
Online Data ◽  
Negative Results ◽  
Application Programming ◽  
And Performance ◽  
Learner Activity ◽  
Programming Interface ◽  
Learning Data

<p style='text-indent:20px;'>With the rising demand for data science skills, the ability to wrangle data programmatically becomes a crucial barrier. In this paper, we discuss the centrality of API (application programming interface) lookup to data wrangling, and how an ontology-structured command menu could facilitate it. We design thumbnail graphics as visual alternatives to explaining data wrangling operations and use a survey to validate their quality. We furthermore predict that thumbnail graphics make the menu more navigable, improving lookup efficiency and performance. Our predictions are tested using Slice N Dice, an online data wrangling tutorial platform that collects learner activity. It includes both non-programmatic and programmatic data wrangling exercises. Participants from a multi-institutional sample (<i>n</i> = 200) were randomly assigned the tutorial either with or without thumbnail graphics. Our results show that thumbnail graphics reduce the need for clarifications, thereby assisting API lookup for novices learning data wrangling. We further present some negative results regarding performance gain and follow up with a discussion on why the differences are subtle and how they can be improved. Last but not least, we complement our statistical results with a qualitative study where we receive positive feedback from our participants on the design and helpfulness of the thumbnail graphics.</p>

scholarly journals Using Twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines

2021 ◽  
pp. postgradmedj-2021-140685
Author(s):  
Robert Marcec ◽  
Robert Likic
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
English Language ◽  
Vaccination Campaign ◽  
Application Programming Interface ◽  
Social Media Platforms ◽  
Application Programming ◽  
Source Of Information ◽  
Programming Interface ◽  
Over Time

IntroductionA worldwide vaccination campaign is underway to bring an end to the SARS-CoV-2 pandemic; however, its success relies heavily on the actual willingness of individuals to get vaccinated. Social media platforms such as Twitter may prove to be a valuable source of information on the attitudes and sentiment towards SARS-CoV-2 vaccination that can be tracked almost instantaneously.Materials and methodsThe Twitter academic Application Programming Interface was used to retrieve all English-language tweets mentioning AstraZeneca/Oxford, Pfizer/BioNTech and Moderna vaccines in 4 months from 1 December 2020 to 31 March 2021. Sentiment analysis was performed using the AFINN lexicon to calculate the daily average sentiment of tweets which was evaluated longitudinally and comparatively for each vaccine throughout the 4 months.ResultsA total of 701 891 tweets have been retrieved and included in the daily sentiment analysis. The sentiment regarding Pfizer and Moderna vaccines appeared positive and stable throughout the 4 months, with no significant differences in sentiment between the months. In contrast, the sentiment regarding the AstraZeneca/Oxford vaccine seems to be decreasing over time, with a significant decrease when comparing December with March (p<0.0000000001, mean difference=−0.746, 95% CI=−0.915 to −0.577).ConclusionLexicon-based Twitter sentiment analysis is a valuable and easily implemented tool to track the sentiment regarding SARS-CoV-2 vaccines. It is worrisome that the sentiment regarding the AstraZeneca/Oxford vaccine appears to be turning negative over time, as this may boost hesitancy rates towards this specific SARS-CoV-2 vaccine.

Sistem Pengawasan Aktifitas Penggunaan Smartphone Android

2018 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Rudianto Rudianto ◽  
Eko Budi Setiawan
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Image Content ◽  
Image File ◽  
Application Programming ◽  
Index Terms ◽  
Programming Interface

Availability the Application Programming Interface (API) for third-party applications on Android devices provides an opportunity to monitor Android devices with each other. This is used to create an application that can facilitate parents in child supervision through Android devices owned. In this study, some features added to the classification of image content on Android devices related to negative content. In this case, researchers using Clarifai API. The result of this research is to produce a system which has feature, give a report of image file contained in target smartphone and can do deletion on the image file, receive browser history report and can directly visit in the application, receive a report of child location and can be directly contacted via this application. This application works well on the Android Lollipop (API Level 22). Index Terms— Application Programming Interface(API), Monitoring, Negative Content, Children, Parent.

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