Social Media Twitter Research Articles

“Lebih Banyak Dramanya Daripada Nyatanya”: Makna, Motif, dan Keberlanjutan Hubungan Pacaran Virtual di Twitter

In the era of digital technology, which is increasingly rapid and developing with the creation of various social media applications, this opens a space for people to get to know each other through these media without meeting in person and building an intimate relationship, known as virtual dating. This virtual dating has become a trend among social media account users and one of the most common social media that is used for virtual dating is Twitter. While the existing literatures related to virtual dating is focused more on how social media is used to find virtual partners, this article explores virtual dating to gain an understanding of the meaning, motives, and sustainability of virtual dating relationships through Twitter.
 This qualitative research was carried out using social media Twitter. Those who participated in this research were ten Twitter account users who have experience virtual dating. They are varied based on their initial place of meeting in the application with their virtual partners (via Twitter, Facebook, and Instagram, and online game Hago) and age (between 18 and 28). Data was collected using in-depth interview and observation techniques via Twitter.
 The study shows that the meaning of virtual dating is broadly categorised into two: positive and negative meanings. The positive meaning is not only related to expressing love like in conventional dating, but also virtual dating is meant as romantic relationship that trains one’s patience, loyalty, and independence. Virtual dating is interpreted negatively since it is considered as a wasting time relationship, is prone to being cheated, and is an ineffective relationship. The underlying motives for someone to have a virtual dating is classified into three, namely (1) interest (such as pampered with gifts by virtual friends and interesting typing style); (2) feelings {such as too carried away by the treatment of virtual friends and high curiosity (kepo) about virtual dating, feelings of insecurity, trust issues}; and (3) real life condition {such as feelings boredom (gabut), not having a circle of friends in the real world (nolep), and having strict parents}. The continuity of virtual dating varies from failed relationships to successful relationship. The former is marked by the end of a relationship (ghosting) due to lost contact, feelings of boredom, not having the effort to meet each other, and having high expectations to the partner. The latter means having a meeting in the real world, even to the point of getting marriage. It is argued in this article that while the feeling and expectation in virtual dating are relatively the same as in real life dating, the former is not real, not until the romantic couple is met in a real life.

Language Styles In Fiersa Besari’s Twitter

This study examines the style of language on Twitter Social Media from the Fiersa Besari account. The purpose of this study is to identify the style of language used by Fiersa Besari in Twitter updates and to describe the types of language style that occurs in Fiersa Besari's Twitter updates. Twitter application users are generally adults and Fiersa Besari is the object of researcher to conduct research related to language style. The researcher uses Qualitative methods because research is based on data in the form of sentences and clauses. The data is taken from several updates from Twitter users to get the right data. To collect data, the researcher uses the theory from Martin Joos (1976). The researcher identifies and describes all the reforms related to language style based on Language Style: Frozen style, Formal, Consultative, Casual, and Intimate style. The types of language styles found in the data from the results of this analysis are Frozen, Formal, Consultative, Casual, and Intimate styles.

Personal Political Branding: Strategi Kampanye Ganjar Pranowo Untuk Pilpres 2024 di Social Media Twitter

In this digital age, social media has become a powerful platform to communicate and influence public opinion. A critical aspect of using social media is its ability to strengthen political branding, which is how a politician or political party builds their image and identity in the eyes of the public. Political branding refers to creating a political image and identity for a candidate, political party, or political movement in society. Ganjar Pranowo, as the Governor of Central Java, utilizes Twitter as a medium to strengthen the value of political branding. This research aims to explain Ganjar Pranowo's political branding strategy through his Twitter social media account. This research uses a qualitative method; then, the researcher uses the Nvivo 12Plus application to analyze the data. The results of this study show that political branding efforts made by Ganjar Pranowo include: First, Ganjar Pranowo presents himself as a religious leader. Second, Ganjar Pranowo presents himself as a leader close to middle to lower-class people. Third, Ganjar Pranowo presents himself as a leader who upholds ethnicity and culture. Fourth, Ganjar Pranowo displays that he is a leader who supports improving the community's economy through UMKM. Fifth, Ganjar Pranowo presents himself as a leader close to the younger generation. Ganjar Pranowo's political branding capability lies in personality attributes. The personal attributes found in the Twitter account @ganjarpranowo include: First, competence as a leader. Second, passion and dedication. Third, leadership style. This research is expected to provide an overview related to political branding from political actors and stakeholders.

Importance of Breast Sensation After Mastectomy: Evidence from Three Sources.

Every year, more than 90,000 U.S. women undergo mastectomy. More than 40% have reconstruction. Following reconstruction, most women experience persistent partial or complete numbness of the reconstructed breasts, and many experience pain. Yet, breast reconstruction procedures focus largely on esthetic outcomes with mixed impact on sensory outcomes and little attention to pain. This study examines whether and how breast sensation is important to women. Conventional content analysis of extant qualitative data from a clinical registry (29 women with prior breast surgery for cancer, 2008-2022), a volunteer community sample (qualitative interviews with 6 women with and 5 without breast cancer, 2019), and from a Twitter social media survey (N = 32, 2022). Functions of the breast identified by women with and without cancer include breastfeeding, sexual function, and femininity. Five interrelated themes on the importance of breast sensation emerged among women with breast cancer history: sexual function, experience of partnered sex or relationship with one's sexual partner, breast embodiment, effect of breast pain on sexual function, and importance to psychological wellbeing. Women, advocates, and clinicians described a lack of patient-physician communication in this domain that exacerbates the negative impact of breast sensation loss on health and wellbeing. Breast sensation is important to women following mastectomy, yet a gap exists in patient-physician communication about the impact of mastectomy and reconstruction on breast sensory function. Lessons for physicians, scientists, and skeptics are conveyed about why the basic integrity of women's bodies matters for practice and science.

Analisis Sentimen Masyarakat Terhadap Perkuliahan Daring di Twitter Menggunakan Algoritma Naive Bayes dan Support Vector Machine

The COVID-19 pandemic has changed the education landscape around the world, resulting in the cessation of in-person teaching and learning activities and encouraging the adoption of online learning systems. Many Indonesians express their opinions and thoughts about online courses through the social media Twitter. Therefore, this study aims to analyze people's sentiment towards online lectures on Twitter using Naïve Bayes and Support Vector Machine (SVM) methods. Data for sentiment analysis is taken from Twitter using the keywords "#college", "#daring", and "#kuliahdaring". This study limits data collection to the range of 2021-2022. A total of 1,260 Tweets were analyzed, with 633 Tweets having positive sentiments and 627 Tweets having negative sentiments. This study uses Naïve Bayes and Support Vector Machine algorithms to classify positive and negative sentiments in Tweets. The results showed that Naïve Bayes algorithm achieved the highest accuracy of 72%, while Support Vector Machine achieved 66% accuracy.

PERAN CITRA MEREK SEBAGAI MEDIASI PENGARUH CELEBRITY ENDORSER DAN VIRAL MARKETING TERHADAP MOTIVASI KEPUTUSAN PEMBELIAN OREO X BLACKPINK

This research aims to examine the role of brand image as a mediator variable in the influence of celebrity endorser and viral marketing on decision motivation to purchase Oreo X Blackpink. This research was designed using explanatory research. Respondents were Blackpink fans who use social media Twitter in Indonesia. The sample used was 112 respondents with purposive sampling as the sampling method. Path analysis is a data analysis method that was used in this research. The results showed that: (1) Celebrity endorser has a significant and positive effect on brand image; (2) Viral marketing has a significant and positive effect on brand image; (3) Celebrity endorser has a significant and positive effect on purchase decision motivation; (4) Viral marketing has a significant and positive effect on purchase decision motivation; (5) Brand image has a significant and positive effect on purchase decision motivation; (6) Celebrity endorser has a significant and positive effect on purchase decision motivation through brand image; (7) Viral marketing has a significant and positive effect on purchase decision motivation through brand image.

Implementation of Naïve Bayes Algorithm in Sentiment Analysis of Twitter Social Media Users Regarding Their Interest to Pay the Tax

Since 2008, tax revenue has failed to reach the target set in the State Budget each year. Until 2021, tax revenue managed to reach the target that had been targeted in the 2021 state budget. In the midst of improving tax revenue, towards the end of February 2023, a case involving the son of a Directorate General of Taxes (DGT) that made the father called by the Corruption Eradication Commission (CEC) to be asked for an explanation of his assets. After the case, there were many calls in the community to stop paying taxes, which was assessed by Tauhid Ahmad as Executive Director of Indef as a form of decreased trust in tax collecting institutions. This can affect the amount of revenue from taxes because trust in the government is one of the factors that tend to affect public compliance in paying taxes. Which can affect the amount of revenue from taxes because trust in the government is one of the factors that tend to affect public compliance in paying taxes. One of the crowded calls is the pros and cons of the tax boycott movement on Twitter. With the pros and cWith the pros and cons of the movement that can affect tax revenues on Twitter social media, an assessment based on sentiment analysis is needed which is divided into positive, neutral, or negative categories. Sentiment analysis in this research is carried out using three variations of Naïve Bayes assisted by the TF-IDF word weighting model, namely Gaussian Naïve Bayes, Multinomial Naïve Bayes, and Bernoulli Naïve Bayes. Then Confussion Matrix is used to evaluate the model by obtaining the accuracy, precission, recall, and f1-score values and the use of Synthetic Minority Oversampling Technique (SMOTE) to handle unbalanced data. The results of this study on unbalanced data, the implementation of Bernoulli Naïve Bayes using the SMOTE technique on a dataset comparison of 80:20 resulted in better performance than the variations of Gaussian and Multinomial Naïve Bayes with accuracy results of 91.03%, precision, 71.11%, recall 71.43%, and f1-score of 71.18%.

PEMODELAN TOPIK TERKAIT BANJIR PADA TWITTER DENGAN MENGGUNAKAN LATENT DIRICHLET ALLOCATION

In this background discusses the topic of tweet about Flooding on Twitter using the keyword "Flood". Tweet data was taken from June 1, 2021 to June 2, 2021 with the number of tweet data obtained, which was 2000 tweets. The number of tweets related to flooding has not been analyzed so that the topics contained in it are not yet known. Research . Modeling topics related to floods in Indonesia on Twitter social media with the LDA method. Research. This study uses experimental methods with several variables to test hypotheses. Then the data is processed with stages, namely web data extraction, preprocessing, feature extraction, topic modeling using latent dirichlet allocation algorithms, visualization, and analysis. Research. The results of the topic coherence stage were carried out a search for the most optimal topic from the 20 topics that had been determined at the beginning. The results of topic coherence for 20 topics concluded that for topic 10 it has a total topic value of 0.41 and has an ideal topic modeling result and is in accordance with the provisions. Conclusion : Based on the results of the discussion of topic coherence, it can be concluded that the most ideal number of topics is topic 10 because it has the highest value compared to other topics. The advice here is to be able to display or get flood information in Indonesia in real time and accurately.

Sentiment Analysis Model Development on E-Money Service Complaints

Technology provides various conveniences for users in many aspects, such as in the commercial business world with the development of financial technology or fintech. In Indonesia, there are fintech services with the most users, namely OVO and DANA. Various kinds of criticisms and suggestions related to deficiencies and weaknesses in OVO and DANA services were submitted by users via social media Twitter. This study aims to analyze and develop a sentiment identification model for user complaints against OVO and DANA services on Twitter. This study uses the four classification algorithm methods: Random Forest, K-NN, Decision Tree, and Naïve Bayes Classifier. The data from social media Twitter is taken from user tweets regarding criticism and complaints on OVO and DANA services. The data obtained was 5,000 instances consisting of 20% positive and 80% negative reviews. Accuracy values represent the study results with 87% for Naive Bayes, 86% for Decision Tree, 91% for K-NN, and 87% for Random Forest. Based on these results, it can be concluded that testing of sentiment analysis on user complaints on OVO and DANA services using the K-NN algorithm is superior to the other algorithms. This research results in a system that combines the four prediction models. The evaluation shows that the developed model can accurately classify user sentiment.

ANALISIS SENTIMEN DI MEDIA SOSIAL TWITTER DENGAN STUDI KASUS KARTU PRAKERJA

Kartu Prakerja is one of the government's flagship programs in providing training to the workforce. In its implementation there is a lot of information scattered, especially on social media Twitter both in the pros and cons of Kartu Prakerja program. Based on information in the form of tweets that have not been analyzed in depth, it is necessary to analyze sentiment on the Kartu Prakerja in order to obtain appropriate information based on the opinions of netizen s on Twitter. This study discusses sentiment analysis of tweet data with the keyword “Kartu Prakerja” which uses data as many as 6658 tweet data taken in the period May 27 - August 5, 2021. This research uses the Naive Bayes Classification method which has several stages, namely data retrieval, data preprocessing, manual labeling, data training and testing. The solution offered in this study is to create an analysis model that can be used to perform sentiment analysis about Kartu Prakerja on Twitter. Based on the results of this study obtained that the calculation of accuracy obtained a value of 86% for training data and 87% for data testing. This study concluded that the Kartu Prakerja has a positive sentiment by Twitter netizens based on the results of Classification that discusses many positive sentiments such as the benefits, effectiveness and addition of the Kartu Prakerja budget.

Phenomenological study about “Remaja Jompo” in autobase @CollegeMenfess users at social media Twitter

Phenomenological study about “Remaja Jompo” in autobase @CollegeMenfess users at social media Twitter

SENTIMEN ANALISIS PENGGUNA TWITTER TERHADAP SEA GAMES 2023 DENGAN METODE NAIVE BAYES

The development of computers and telecommunications has made people often express their opinions through social media Twitter. As recently, after the 2023 SEA Games were held in Cambodia, various kinds of tweets appeared from various sports. From this opinion, it can be seen that the country's success in holding the Sea Games by conducting a sentiment analysis. So this research was conducted to find out how the public opinion regarding the holding of the 2023 SEA Games using the Naive Bayes Classifier algorithm. The data used was 1888 data obtained by crawling Twitter data using the keywords SEA Games and RapidMiner tools. The division of training data and testing data is carried out into two comparisons, namely 50:50 and 60:40. The best accuracy results are obtained with a data comparison of 50:50, which is 70.15%, while a data comparison of 60:40 has a smaller result, namely 70.07%.

PERANCANGAN APLIKASI KLASIFIKASI SENTIMEN BERBASIS WEB TERHADAP MATA UANG KRIPTO

Sentiment analysis is a collection of opinions that studies opinions about something like an issue, product, event or topic. In deciding to choose cryptocurrency, the majority of Indonesian people tend to only read the latest reviews about cryptocurrencies and even a few will lead to wrong conclusions. To analyze cryptocurrency sentiment easily and quickly, a sentiment analysis system with a convenient and easy-to-use interface is required. The application to analyze this sentiment is web-based which is made using the PHP (Hypertext Preprocessor) framework by applying the MVC (Model View Controller) concept. This application can import data in CSV format which is then processed into an application using the nave Bayes algorithm in the sentiment classification process which is then evaluated by the confusion matrix method. The reviews are taken from social media twitter and it is hoped that this application will be able to provide classification information accurately, precisely and reliably.

Machine Learning Identification on Twitter Towards Combating Covid-19 (SARS-CoV-2): Pandemic Attacks and Urban Resilience in Indonesia

The purpose of this study is to detect the topic and discourse of the development of covid-19 and its resistance on social media Twitter in Indonesia. This research method is surveyed through Machine Learning to filter, track, and predict the spread of Covid-19 (SARS-CoV-2) on Twitter, and is assisted by Nvivo 12 pro and Ncapture analysis tools to collect data from big data on Twitter. The results of this study show ML (Machine Learning) plays a role in fighting the virus, especially looking at it from the perspective of screening, forecasting, and vaccines spread across various social media accounts on Twitter. A comprehensive survey of ML algorithms and models used in covid-19 (SARS-CoV-2) development expeditions on Twitter can help fight the virus. This research shows that the hashtag #lawancovid19 (Fight Covid-19) has relevance to several new hashtags that are still relevant in campaigning against covid-19 on Twitter to combat community and urban vulnerabilities in Indonesia. Collectively, characteristics such as Tagar #ayovaksin (let's get vaccinated), #jagajarak (keep your distance), and #ayopakaimasker (let's wear masks) support the resistance to covid-19 in Indonesia the account (@Username) that is most often @mention in the covid-19 cloud l action is the account of the @jokowi (President of Indonesia). The social media movement (Twitter) in encouraging community resilience and urban resilience through digital communication against Covid-19 (SARS-CoV-2) has predominantly succeeded in shaping understanding, behavior, and prudence in interacting directly in public spaces. This is important in the context of assessing messages and communications within the overall social media (Twitter) communication activities in response to online resistance measures in support of public health as well as to digitally address the global pandemic. This research contributes to providing insight into the dynamics of covid-19 (SARS-CoV-2) combat communication on Social-Media (Twitter) and supporting public health measures.

Sentiment Analysis for Indonesian Salt Policy uses Naïve Bayes and Information Gain Methods

Salt production is one of the concerns of the Indonesian government. Several government policies such as salt imports have had a major impact on local salt farmers. Other factors are due to the increased demand for salt, decreased domestic salt production which is unfavorable due to weather factors, and the price of imported salt is lower than that of local salt. Many people express their opinions regarding the salt import policy, via Twitter social media. Sentiment analysis can be applied to analyze tweets or writings by the public regarding salt import policies and classify the data. This study uses the naïve Bayes classifier algorithm model as a sentiment classification algorithm on Twitter social media tweets. The classification process uses the Naïve Bayes algorithm. The feature extraction and weighting method is the TF-IDF method. Not all of the features resulting from the TF-IDF process are used, so feature selection is carried out using the information gain method. Model testing was carried out 5 times with 500 data, using feature selection and without feature selection. Without feature selection, the highest accuracy result is 84% at K=4, while without feature selection it produces an accuracy of 71% at K=3, so there is an increase of 13%.

Is the Indonesian Government Islamophobic? Studies Using Social Network Analysis

This study aims to map the narratives, networks, and influential figures on social media Twitter after the arrest of suspected terrorists (Farid Okbah cs and Sunardi) by Detachment 88 anti-terror in Indonesia. We use the social network and content analysis methods in collecting and analyzing research data. The results of this study show that the anti-Islamic political group with government (opposition) is currently trying to make a narrative that the Indonesian government is Islamophobic, by utilizing the momentum of suspected terrorist arrests in Farid Okbah and Sunardi by Detachment 88 by shifting the issue of terrorist arrests from the JI group who are also members of MUI with the demand for dissolving the Government Institution Detachment 88 Anti-terror. The social network map shows that there were three groups engaged in the arrest of suspected terrorists Farid Okbah and Sunardi, 1) Muslim Group Cyber Army (MCA); 2) Pro-government buzzer group through Denny Siregar’s account; 3) The provocateurs group. The arrest of Farid Okbah and Sunardi turned into political issues, Islamic groups (MUI) vs. Government (Detachment). The arrest of Farid Okbah by Detachment 88 was dominated by #saminawaathonaibhrs and #Islamophobia, while the dominant narrative after the suspected arrest of Sunardi’s is #PKItangkapulama (PKI Arrest Ulama) and #dukungMUI (Support MUI), both of these things showed an effort to accompany the issue to the political narrative of the opposition group to weaken the government with the narrative that discriminated government of Islamic groups.
 Keywords: Islamophobia, social network analysis, narrative analysis, social media

Analisis Sentimen Mengenai Kualitas Layanan Jasa Ekspedisi Shopee Xpress Pada Media Sosial Twitter

Technological advances in Indonesia has developed rapidly so that it cannot be separated from people's lives. This technological advancement has had a significant impact on e-commerce and increased usage of delivery services. The more shipping services available, the tighter the competition will be, so the freight forwarders must prepare well if they want to survive in this business competition. Shopee Xpress is one of the expedition services in Indonesia which was developed directly by Shopee so that every package that uses this expedition will be sent and handled directly by the official team from Shopee. However, many customers complain about Shopee Xpress expedition services regarding late delivery, untracked, damaged or lost packages and unfriendly employees. This study aims to understand consumer opinion on the quality of Shopee Xpress services on Twitter using positive and negative sentiments. Research data was collected for 3 months from 01 November 2022 to 30 January 2023. The data obtained was then processed using the Naïve Bayes Classifier algorithm with a sentiment analysis method based on the dimensions of service quality. The word cloud is used to describe the words that appear most frequently in each dimension of service quality based on their sentiments. The results show that the model produces an accuracy value of 86.33% which indicates that the model has described the accuracy of the data properly and is valid. Negative sentiment dominates consumer opinion regarding Shopee Xpress with the reliability dimension being the most discussed. Furthermore, based on word cloud visualization, there are several things that consumers often complain about in each dimension as a material for consideration to improve the quality of Shopee Xpress services. The results of research related to Shopee Xpress consumer opinion on Twitter social media can be used by the company as material for consideration to improve and improve the quality of services provided in order to maintain customer loyalty.

DINAMIKA PENGGUNA MEDIA SOSIAL TERKAIT CALON PRESIDEN PEMILIHAN UMUM 2024

Ahead of the 2024 general election (Pemilu), a number of political parties have declared their presidential candidates. However, there are pros and cons on Twitter social media regarding Anies Baswedan's nomination by the National Democratic Party (Nasdem). This study aims to determine the dynamics of Twitter social media users after Anies Baswedan's declaration as a presidential candidate for RI 2024. Descriptive qualitative research methods are used in answering the problem formulation. The author uses New Media theory and Propaganda theory and is assisted by a social media analysis tool in the form of Netray Media Monitoring to analyze the dynamics of Twitter social media users. The results of the study show that the dynamics that occur are due to the pros and cons regarding Anies Baswedan's nomination as a 2024 presidential candidate by the Nasdem Party. The pros and cons created negative sentiment and positive sentiment towards Anies which then made Anies busy being discussed on Twitter social media, causing Anies to become the most popular on Twitter social media

Social Media and the Public’s Involvement in the Disaster’s Narrative (Case Study: Bantul Regency, Indonesia)

This research aims to discuss the disaster narration during the PPKM period on the Bantul Regency's social media. This research uses the qualitative method from interviews and social media data analyzed by the Computer Assisted Qualitative Data Analysis (CAQDS). The data were showcased in crosstab analysis and word cloud analysis. The result shows that 1 Bantul District has succeeded in building a community understanding of crises rather than increasing the reputation of actors. The narrative built through crisis knowledge created by the Regional Disaster Relief Agency (BPBD) Bantul Data and Information Center (Pusdalops) needs to be better informed. 2) Then, in the social media dimension of Twitter, COVID-19 data is only presented with updates without showing collaboration between actors. 3) public interaction on BPBD Bantul's Twitter social media was primarily positive, with 63% (supporting all parties in tackling COVID-19), damaging 21% (distrust in COVID-19 data), and neutral 11% (impartial). In word cloud analysis, the dominance of words often used as narratives is Bantul with 31 counts, followed by data, COVID-19, district PPKM, etc.

Analisis Pragmatik Fungsi Tindak Tutur dalam Kasus Ujaran Kebencian Ferdinand Hutahaen

Speech is part of action. Making a statement incorrectly can lead someone into a case of hate speech. This study aims to describe speech acts in the speech of characters/people who are used as legal cases for hate speech. The results of this study are useful for helping the public to understand and provide knowledge to the public to be careful in speaking so as not to be subject to legal action for hate speech. This research uses descriptive qualitative research. The pragmatic analysis used is speech act analysis. The data for this study are uploads on social media Twitter from someone who has been sued for hate speech, namely Ferdinand Hutahaen. The results of this study are: the utterance is declarative in nature which affects the involvement of the utterance in the lawsuit. Hate speech is a choice of language that has a negative connotation in expressing the speaker's opinion about something.Keywords: pragmatics; speech act; hate speech.