scholarly journals Mapping the political landscape of Persian Twitter: The case of 2013 presidential election

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171983523 ◽  
Author(s):  
Emad Khazraee
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Network Analysis ◽  
Sentiment Analysis ◽  
Presidential Election ◽  
The Political ◽  
Topic Detection ◽  
Political Landscape ◽  
English Speaking

The fallacy of premature designations such as “Iran's Twitter Revolution” can be attributed to the empirical gap in our knowledge about such sociotechnical phenomena in non-Western societies. To fill this gap, we need in-depth analyses of social media use in those contexts and to create detailed maps of online public environments in such societies. This paper aims to present such cartography of the political landscape of Persian Twitter by studying the case of Iran's 2013 presidential election. The objective of this study is twofold: first, to fill the empirical gap in our knowledge about Twitter use in Iran, and second, to develop computational methods for studying Persian Twitter (e.g., effective methods for analyzing Persian text) and identify the best methods for addressing different issues (e.g., topic detection and sentiment analysis). During Iran's 2013 presidential election, three million tweets were collected and analyzed using social network analysis and machine learning. The findings provide a more nuanced view of the political landscape of Persian Twitter and identify patterns in accordance with or in contrast to those identified in the English-speaking Twittersphere around the 2013 presidential election. Persian Twitter was dominated by micro-celebrities, whereas institutional elites dominated English discourse about Iran on Twitter. The results also illustrate that Persian Twitter in 2013 was predominantly in favor of reformists. Finally, this study demonstrates that sentiment analysis toward political name entities can be used efficiently for mapping the political landscape of conversation on Twitter.

A social network analysis of Canadian food insecurity policy actors

2018 ◽  
Vol 79 (2) ◽  
pp. 60-66 ◽  
Author(s):  
Lynn McIntyre ◽  
Geneviève Jessiman-Perreault ◽  
Catherine L. Mah ◽  
Jenny Godley
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Nongovernmental Organizations ◽  
Food Insecurity ◽  
The Political ◽  
Policy Entrepreneurs ◽  
Policy Actors ◽  
Political Landscape ◽  
Potential Food

Purpose: This paper aims to: (i) visualize the networks of food insecurity policy actors in Canada, (ii) identify potential food insecurity policy entrepreneurs (i.e., individuals with voice, connections, and persistence) within these networks, and (iii) examine the political landscape for action on food insecurity as revealed by social network analysis. Methods: A survey was administered to 93 Canadian food insecurity policy actors. They were each asked to nominate 3 individuals whom they believed to be policy entrepreneurs. Ego-centred social network maps (sociograms) were generated based on data on nominees and nominators. Results: Seventy-two percent of the actors completed the survey; 117 unique nominations ensued. Eleven actors obtained 3 or more nominations and thus were considered policy entrepreneurs. The majority of actors nominated actors from the same province (71.5%) and with a similar approach to theirs to addressing food insecurity (54.8%). Most nominees worked in research, charitable, and other nongovernmental organizations. Conclusions: Networks of Canadian food insecurity policy actors exist but are limited in scope and reach, with a paucity of policy entrepreneurs from political, private, or governmental jurisdictions. The networks are divided between food-based solution actors and income-based solution actors, which might impede collaboration among those with differing approaches to addressing food insecurity.

Smart Campus

2021 ◽  
pp. 161-169
Author(s):  
Sushant Keni ◽  
Priyanka Jadhav ◽  
Mayur Patil ◽  
Prof. Sonal Chaudhari
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Network Analysis ◽  
Learning Algorithm ◽  
Third Parties ◽  
Machine Learning Algorithm ◽  
Good Service ◽  
Smart Campus ◽  
Recruitment System

We evaluate the feasibility of using Facebook data to enhance the effectiveness of a recruitment system, especially for résumé verification and recognize the personality by using social network analysis methods. In the industries employee’s personality is very important in the workplace which will help to growth of the company and give more good service to the client. Currently resume verification is based on trustful third parties who does background verification. Based on this report is sent to the company who is hiring the employee decides to keep employee or not. This manual system usually takes lots of time and this system generally wont display candidates’ nature towards society (in short how he behaves in society weather he posts something wrong on social media in simple words his/her personality). Social media now a days is huge platform where user generally spends too much time on social media like Facebook, LinkedIn etc. like posting a page, commenting, liking the post, certification uploading, adding friends. We are going to design such a system that verifies genuineness of user by scraping or exploring data from Facebook or LinkedIn or both. we are exploring post of person and classifies it into is it technology related, violence related and many more what are the comments he gives on his post how he reacts his language of handling a query will be parsed and classified using machine learning algorithm of previously trained dataset using SVM. And at the end we will show this information to the company to make their own decision based on this result.

scholarly journals THE IMPLEMENTATION OF THE MACHINE LEARNING ALGORITHM FOR THE SENTIMENT ANALYSIS OF INDONESIA’S 2019 PRESIDENTIAL ELECTION

2021 ◽  
Vol 22 (1) ◽  
pp. 78-92
Author(s):  
GA Buntoro ◽  
R Arifin ◽  
GN Syaifuddiin ◽  
A Selamat ◽  
O Krejcar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Presidential Election ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithm ◽  
Presidential Candidates ◽  
N Gram

In 2019, citizens of Indonesia participated in the democratic process of electing a new president, vice president, and various legislative candidates for the country. The 2019 Indonesian presidential election was very tense in terms of the candidates' campaigns in cyberspace, especially on social media sites such as Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, etc. The Indonesian people used social media platforms to express their positive, neutral, and also negative opinions on the respective presidential candidates. The campaigning of respective social media users on their choice of candidates for regents, governors, and legislative positions up to presidential candidates was conducted via the Internet and online media. Therefore, the aim of this paper is to conduct sentiment analysis on the candidates in the 2019 Indonesia presidential election based on Twitter datasets. The study used datasets on the opinions expressed by the Indonesian people available on Twitter with the hashtags (#) containing "Jokowi and Prabowo." We conducted data pre-processing using a selection of comments, data cleansing, text parsing, sentence normalization and tokenization based on the given text in the Indonesian language, determination of class attributes, and, finally, we classified the Twitter posts with the hashtags (#) using Naïve Bayes Classifier (NBC) and a Support Vector Machine (SVM) to achieve an optimal and maximum optimization accuracy. The study provides benefits in terms of helping the community to research opinions on Twitter that contain positive, neutral, or negative sentiments. Sentiment Analysis on the candidates in the 2019 Indonesian presidential election on Twitter using non-conventional processes resulted in cost, time, and effort savings. This research proved that the combination of the SVM machine learning algorithm and alphabetic tokenization produced the highest accuracy value of 79.02%. While the lowest accuracy value in this study was obtained with a combination of the NBC machine learning algorithm and N-gram tokenization with an accuracy value of 44.94%. ABSTRAK: Pada tahun 2019 rakyat Indonesia telah terlibat dalam proses demokrasi memilih presiden baru, wakil presiden, dan berbagai calon legislatif negara. Pemilihan presiden Indonesia 2019 sangat tegang dalam kempen calon di ruang siber, terutama di laman media sosial seperti Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, dll. Rakyat Indonesia menggunakan platfom media sosial bagi menyatakan pendapat positif, berkecuali, dan juga negatif terhadap calon presiden masing-masing. Kampen pencalonan menteri, gabenor, dan perundangan hingga pencalonan presiden dilakukan melalui media internet dan atas talian. Oleh itu, kajian ini dilakukan bagi menilai sentimen terhadap calon pemilihan presiden Indonesia 2019 berdasarkan kumpulan data Twitter. Kajian ini menggunakan kumpulan data yang diungkapkan oleh rakyat Indonesia yang terdapat di Twitter dengan hashtag (#) yang mengandungi "Jokowi dan Prabowo." Proses data dibuat menggunakan pilihan komentar, pembersihan data, penguraian teks, normalisasi kalimat, dan tokenisasi teks dalam bahasa Indonesia, penentuan atribut kelas, dan akhirnya, pengklasifikasian catatan Twitter dengan hashtag (#) menggunakan Klasifikasi Naïve Bayes (NBC) dan Mesin Vektor Sokongan (SVM) bagi mencapai ketepatan optimum dan maksimum. Kajian ini memberikan faedah dari segi membantu masyarakat meneliti pendapat di Twitter yang mengandungi sentimen positif, neutral, atau negatif. Analisis Sentimen terhadap calon dalam pemilihan presiden Indonesia 2019 di Twitter menggunakan proses bukan konvensional menghasilkan penjimatan kos, waktu, dan usaha. Penyelidikan ini membuktikan bahawa gabungan algoritma pembelajaran mesin SVM dan tokenisasi abjad menghasilkan nilai ketepatan tertinggi iaitu 79.02%. Manakala nilai ketepatan terendah dalam kajian ini diperoleh dengan kombinasi algoritma pembelajaran mesin NBC dan tokenisasi N-gram dengan nilai ketepatan 44.94%.

scholarly journals Sentiment Severity on Location-Based Social Network (LBSN) Data of Natural disasters

2020 ◽  
Vol 8 (5) ◽  
pp. 4219-4224
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Twitter Data ◽  
Location Based Social Network ◽  
Negative Sentiment ◽  
Disaster Event ◽  
Extract Information

Social media emerged as one of the key components to reach disaster affected people, as they supplement planning and operational coordination. Sentiment analysis was expended to identify, extract or characterize subjective information, such as opinions, expressed in a tweet. The sentiment expressed is analyzed and is classified as positive or negative sentiment, which is not versatile enough to capture the exact sentiment conveyed by the user. Opinion mining is a machine learning process used to extract information conveyed by the user in the form of text. In this paper, the lexical analysis to sentiment analysis of twitter data is employed. Conventionally, the sentiment is conveyed using the polarity of the data but in this paper, sentiment intensity is employed to convey the sentiments. Performing sentiment analysis on tweets gives us the sentiment intensity conveyed by the user, which in turn is used to calculate the severity of the disaster event specified by the user. Further, it is also used to classify the tweets based on their severity. This paper proposes a methodology to extract relevant sentiment information from Location Based Social Network (LBSN) and suggests a unique scale to classify this information to help disaster management authority.

scholarly journals New Media and Nationalism in Indonesia: An Analysis of Discursive Nationalism in Online News and Social Media after the 2019 Indonesian Presidential Election

2021 ◽  
Vol 37 (2) ◽  
pp. 289-304
Author(s):  
Didik Haryadi Santoso ◽  
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
New Media ◽  
Presidential Election ◽  
Time Frame ◽  
Online News ◽  
Physical Interaction ◽  
High Level

Nationalism is an issue that is often contested in a political rally in various countries. Nationalism is generally used to describe two phenomena: first, the attitude of members of the nation when they care about their national identity. Second, it can be defined as any action performed by members of the nation to sustain their self-determination or political sovereignty. In the era of conventional media, nationalism was created from the dynamics of physical interaction, human to human. However, in the new media era, nationalism has turned into "human to technology to human". This leads to a dynamic that never happened before. To capture this, online news and social media data were captured using the SNA (Social Network Analysis) method, in collaboration with astramaya.id that saw 19 online news items listed. Data were also collected from Facebook (3,376 mentions), Instagram (3,417 mentions), Twitter (160,432 mentions), and YouTube (1,699 mentions). The time frame, July 2019 to July 2020, takes into account the high level of discussion on nationalism after the presidential election and Covid-19. This research found that: first, Indonesia’s Nationalism has divided into two caps; second, a non-human social media account gives a significant contribution to these cleavages; third, primordial sentiments take determining effect for every actor in generating cleavage. Keywords: Nationalism, online news, social media, social network analysis, Indonesia.

scholarly journals Identifying social media user demographics and topic diversity with computational social science: a case study of a major international policy forum

2020 ◽  
Vol 3 (1) ◽  
pp. 167-188 ◽  
Author(s):  
John Brandt ◽  
Kathleen Buckingham ◽  
Cody Buntain ◽  
Will Anderson ◽  
Sabin Ray ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sustainable Development ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Computational Social Science ◽  
Economic Benefits ◽  
Sustainable Land Use ◽  
Demographic Differences

AbstractWhen the world’s countries agreed on the 2030 Agenda for Sustainable Development, they recognized that equity and inclusion should be at the center of implementing the 17 Sustainable Development Goals (SDGs). SDG 15, which calls for protecting, restoring, and promoting the sustainable use of terrestrial ecosystems, has spurred commitments to restore 350 million hectares of land by 2030. These commitments, primarily made in a top-down manner at the international scale, must be implemented by actively engaging individual landholders and local communities. Ensuring that diverse and marginalized audiences are engaged in the land restoration movement is critical to equitably distributing the economic benefits of restoration. This publication uses social network analysis and machine learning to understand how important the voices of Africans, women, and young people are in governing restoration in Africa. We analyze location- and machine learning-identified demographics from Twitter data collected during the Global Landscapes Forum (GLF), which is the world’s largest platform for promoting sustainable land use practices. Our results suggest that convening the GLF in Nairobi, Kenya elevated the voices of African leaders in comparison to the previous GLF in Bonn, Germany. We also found significant demographic differences in topic-level engagement between different ages, races, and genders. The primary contributions of this paper are a novel methodology for quantifying demographic differences in social media engagement and the application of social media and social network analysis to provide critical insights into the inclusivity of a large political conference aimed at engaging youth and African voices.

Politics of hashtags: Social network analysis of online contestation in the 2019 Indonesia presidential election

2021 ◽  
pp. 151-170
Author(s):  
Caroline Paskarina ◽  
Rina Hermawati Nuraeni
Keyword(s):  
Social Media ◽  
Social Network ◽  
Civil Society ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Presidential Election ◽  
Active Role ◽  
Public Issues ◽  
Strategic Role

This article uses social network analysis of online contestation on Twitter from September 2018 to April 2019 to reveal how netizens' engagement in election de-bates is polarized by the politics of hashtags. This study finds that hashtags are operated to construct dichotomist debate focusing on both presidential candi-dates' figure. This finding indicates: first, the weak position of citizens in deliberat-ing public issues; second, the ineffectiveness of social media, especially Twitter as an online forum for articulating public issues; and third, online influencers, who create and propagate hashtags, play a strategic role in deliberating public issues. Strengthening the role of social media needs to be combined with contemporary citizenship political strategies that can extend access for civil society and online influencers to play an active role in articulating public issues more argumentative-ly.

scholarly journals Studying the Community of Trump Supporters on Twitter during the 2020 US Presidential Election via Hashtags #maga and #trump2020

2021 ◽  
Vol 2 (4) ◽  
pp. 709-731
Author(s):  
Huu Dat Tran
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Presidential Election ◽  
Political Context ◽  
Future Research ◽  
The Political ◽  
Donald Trump ◽  
The Social ◽  
Us Presidential Election

(1) The study investigated the social network surrounding the hashtags #maga (Make America Great Again, the campaign slogan popularized by Donald Trump during his 2016 and 2020 presidential campaigns) and #trump2020 on Twitter to better understand Donald Trump, his community of supporters, and their political discourse and activities in the political context of the 2020 US presidential election. (2) Social network analysis of a sample of 220,336 tweets from 96,820 unique users, posted between 27 October and 2 November 2020 (i.e., one week before the general election day) was conducted. (3) The most active and influential users within the #maga and #trump2020 network, the likelihood of those users being spamming bots, and their tweets’ content were revealed. (4) The study then discussed the hierarchy of Donald Trump and the problematic nature of spamming bot detection, while also providing suggestions for future research.

scholarly journals Polish and Swedish journalist-politician Twitter networks: Who are the gatekeepers?

2018 ◽  
Vol 11 (2) ◽  
pp. 129-150
Author(s):  
Elena Johansson ◽  
Jacek Nożewski
Keyword(s):  
Social Media ◽  
Social Network ◽  
Network Analysis ◽  
Comparative Study ◽  
Technological Development ◽  
The Political ◽  
Extra Space ◽  
Political Sources ◽  
The Relationship

The relationship between journalists and political sources takes different forms and extends from adversarial to advocating. Th e question which side ‘leads the tango’ has always been central to this approach. Since technological development has led to hybridization of themedia systems, the nature of communication has been reshaped in many ways. The emergence of social media has challenged the journalistic profession, especially journalists’ role as gatekeepers, but provided extra space for interaction with sources. Increasing professionalization of politics has reinforced the role of press secretaries/advisers. This is a comparative study of interaction among Polish and Swedish journalists, ministers, and press secretaries in Twitter provided by network analysis and three social network concepts as density, modularity, and centralization. In this analysis, a more influential position is conceptualized in terms of ‘communicative resources’ or ‘accumulated capacity’. Swedish journalists have more opportunities to act as gatekeepers or ‘key users’ in the Twitter network; in Poland, it is rather the political side.

ANALISIS PERINGKAT TOP BRAND OJEK ONLINE MENGGUNAKAN JEJARING SOSIAL PERCAKAPAN TWITTER

2019 ◽  
Vol 24 (2) ◽  
pp. 88-104
Author(s):  
Ilham Aminudin ◽  
Dyah Anggraini
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Path Length ◽  
Average Path Length ◽  
Average Degree ◽  
Clustering Coefficient ◽  
R Programming

Banyak bisnis mulai muncul dengan melibatkan pengembangan teknologi internet. Salah satunya adalah bisnis di aplikasi berbasis penyedia layanan di bidang moda transportasi berbasis online yang ternyata dapat memberikan solusi dan menjawab berbagai kekhawatiran publik tentang layanan transportasi umum. Kemacetan lalu lintas di kota-kota besar dan ketegangan publik dengan keamanan transportasi umum diselesaikan dengan adanya aplikasi transportasi online seperti Grab dan Gojek yang memberikan kemudahan dan kenyamanan bagi penggunanya Penelitian ini dilakukan untuk menganalisa keaktifan percakapan brand jasa transportasi online di jejaring sosial Twitter berdasarkan properti jaringan. Penelitian dilakukan dengan dengan mengambil data dari percakapan pengguna di social media Twitter dengan cara crawling menggunakan Bahasa pemrograman R programming dan software R Studio dan pembuatan model jaringan dengan software Gephy. Setelah itu data dianalisis menggunakan metode social network analysis yang terdiri berdasarkan properti jaringan yaitu size, density, modularity, diameter, average degree, average path length, dan clustering coefficient dan nantinya hasil analisis akan dibandingkan dari setiap properti jaringan kedua brand jasa transportasi Online dan ditentukan strategi dalam meningkatkan dan mempertahankan keaktifan serta tingkat kehadiran brand jasa transportasi online, Grab dan Gojek.

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