Popular Twitter Research Articles

The Effectiveness of Twitter As an Interpersonal Communication Media Among Youth in Ambon City

Humans are social beings who need each other and can’t live without others. Communications mainly of interpersonal communication is one requirement that can’t be avoided. With a growing period, the communication can not only be done face to face, but can also be through the medium of communication. One of them is the internet. Twitter is one of the most popular online community, whose members are mostly teenagers can relate to each other to communicate. Twitter's popularity has extended throughout the world and attract the attention of many large communities of Indonesia. Interpersonal Communication Effective communication is needed in order to run well between the two parties. Therefore, research must be done in order to know the effectiveness Twitter as a medium of interpersonal communication among teenagers. By using descriptive quantitative methods, it can be seen that interpersonal communication through Twitter media among adolescents in Ambon city is effective.
 Keywords: Effectiveness of Interpersonal Communication, Twitter, Media

Relationship of Altmetric Attention Score, Twitter Performance, and Dimensions Badge Value With Traditional Metrics in Top-Cited Anterior Cruciate Ligament Research Studies.

Alternative citation metrics-such as the Altmetric Attention Score (AAS), number of tweets (TN), and dimensions badge value (DBV)-are emerging as new options for assessing the value of scientific works. To analyze the AAS of highly cited articles on the anterior cruciate ligament (ACL) and to assess the relationship between alternative and traditional metrics such as journal rankings and article citation performance. Cross-sectional study. A search was conducted using the Web of Science Core Collection of databases with "anterior cruciate ligament" as the search term. Full-text articles published between 2011 and 2021 were reviewed, and the top 100 cited articles were determined. The articles were analyzed by publication year, study design, research topic, journal impact factor, journal h-index, number of total citations (TC), recent citations (RC) (ie, citations in the latest 2 years), and average citation per year (ACY), as well as AAS, TN, and DBV. For articles in the top 100 list, the median TC was 160 (interquartile range [IQR], 117-561) and the median AAS was 24.50 (IQR, 1-730). A higher AAS score was achieved by articles on return to sports and anterolateral ligament (P < .05). The AAS and TN were significantly and positively correlated with the RC (r = 0.459 and P = .001; r = 0.438 and P = .001, respectively) and ACY (r = 0.363 and P = .001; r = 0.393 and P = .001, respectively). Alternative metrics were linked to traditional metrics but were not a direct representation of bibliometrics. The AAS was not correlated with TC numbers in the ACL research. Higher AAS and Twitter popularity of an article were related to receiving high-volume RC performance. The DBV represented a stronger correlation with traditional metrics than the AAS.

DEPRESSION DETECTION ON SOCIAL MEDIA TWITTER USING XLNET METHOD

Depression is a serious mental illness. Depression is usually characterized by feelings of sadness, hopelessness, anxiety, restlessness, and even loss of life. However, not everyone who experiences depression can get professional treatment. If depression is left unchecked, it can worsen the mental health conditions experienced by a person. Social media, one of which is the increasingly popular twitter can be utilized to help deal with the problem of undetected mental illness. Based on tweets made by a person twitter social media can be one of the sources to detect depression using the XLNet method. XLNet is one of the NLP (Natural Language Processing) techniques based on machine learning models on text. Based on several tests that have been carried out during the research such as testing various tuning hyper-parameters with different values on the XLNet model, it achieves a good performance value with an average accuracy value of 93.33%.

Defining Southeast Asia Through The Democratization of Technology

The development of technology has enabled the democratization of technology that helps minority groups to disseminate their own discourse outside the control of the dominant media. Through an analysis on a series of popular Twitter tweets made as responses towards the announcement of a Disney animated film Raya and the Last Dragon, this article explores how internet users who identify themselves as Southeast Asian respond to the representations of Southeast Asia produced by a multinational company. Using critical discourse analysis from van Dijk and representation theory, this article examines how popular tweets constructed a discourse on Southeast Asian representation by using their authority as 'Southeast Asian' to construct alternative representations in response to the representations created by popular media and how these reached other internet users. The results of the analysis show that while the representation discourse on the tweets was well received by internet users who identify themselves as 'Southeast Asian', some aspects used to construct the 'Southeast Asian' identity were disapproved of by some users who refused to associate themselves with the identity.

The First Year of Hematology Oncology Fellows -- a Social Media Community for Hematology/Oncology Trainees

Background: While resources exist to address the needs of graduate medical trainees, online resources to assist trainees with specialty-specific professional development have not been fully developed or described in the past. The Hematology-Oncology Fellows Network (@HemOncFellows) is an online community for hematology-oncology trainees in all stages of training, including medical students, residents, fellows, and doctoral students. The network's goals are to provide professional development in hematology-oncology, create a community and safe space for hematology-oncology trainees, and amplify the voices and needs of trainees. In addition, the network is contributing to educational development of fellows by conducting twitter spaces, which are informal discussions on twitter, educational and professional development. Methods: The Hem Onc Fellows community was founded in February of 2021. Demographics of newsletter subscribers were gathered, and qualitative and quantitative analysis was performed. Twitter analytics data was used to see the outreach and engagement with the @hemoncfellows twitter account. Results: Since its inception, the Hem-Onc Fellows Network has grown to more than 3400 followers. The highest impressions per month have been 93.4K with mean twitter 263 impressions per tweet in the month of July 2022. The mean engagement per tweet was 3.4%. The network hosts Twitter Spaces (TS) with guests every two weeks and distributes a newsletter. During the first season, 22 TS were held between July 2021 and June 2022. The average attendance of twitter spaces is 49.6 persons per session (range 22-187). The most popular twitter spaces events were pre ASH and pre ASCO special events. The hem-onc fellows network contributed to two professional development opinions in ASH hematopoiesis and two in ASCO connections. One hundred twenty-one people subscribed to the newsletter. 53% of subscribers were male. 34% were internal medicine residencies, 31% were hematology-oncology fellows, 9% were attendings, 5% were Advanced Hematology-Oncology Fellow and 5% were medical students. Subscribers were from 21 different countries. Most participants were United States-based, but there were participants from 21 countries worldwide. Conclusions: We demonstrate that implementing a trainee network on Twitter attracted participation and engagement from a diverse pool of trainees worldwide. TS represents a novel educational tool for engaging trainees and facilitating professional development. We expect further expansion and engagement continuing to the season two of hem-onc fellows network Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

TF-IDF Weighting to Detect Spammer Accounts on Twitter based on Tweets and Retweet Representation of Tweets

Twitter is a social media service that is often used (popular) as a means of communication between users. Twitter's popularity makes spammers spam for personal purposes and gains. Bot spammers are user abuse on Twitter social media. Spammers spread spam repeatedly to other users. This spam is done with the aim of achieving trending topics. Spam activity is carried out by imitating the behavior patterns of real users so that they are not detected as acts of Twitter abuse. in this paper proposed a TF-IDF weighting to detect spammer accounts on Twitter based on tweets and retweet representation of tweets. The purpose of this study is to detect Bot Spammers or Humans using a classification technique using the Naive Bayes algorithm. The best experimental results in the division of 70% training data and 30% test data obtained 92% accuracy with precision and recall of 100% and 87.5%, respectively. This shows that it has successfully detected spammer accounts on Twitter.

A Comparative Study for Supervised Learning Algorithms to Analyze Sentiment Tweets

Twitter popularity has increasingly grown in the last few years, influencing life’s social, political, and business aspects. People would leave their tweets on social media about an event, and simultaneously inquire to see other people's experiences and whether they had a positive/negative opinion about that event. Sentiment Analysis can be used to obtain this categorization. Product reviews, events, and other topics from all users that comprise unstructured text comments are gathered and categorized as good, harmful, or neutral using sentiment analysis. Such issues are called polarity classifications. This study aims to use Twitter data about OK cuisine reviews obtained from the Amazon website and compare the effectiveness of three commonly used supervised learning classifiers, Naive Bayes, Logistic Regression, and Support Vector Machine. This is achieved by using two method of feature selection involving count Vectorizer and Term-Frequency-Inverse Data Frequency. The findings showed that the support vector machine classifier had achieved the highest accuracy of 91%, by feature selection: Count Vectorizer. But it is time consuming. For both accuracy and execution time concentrates, logistic regression is recommended.

Analysing Hate Speech against Migrants and Women through Tweets Using Ensembled Deep Learning Model.

Twitter's popularity has exploded in the previous few years, making it one of the most widely used social media sites. As a result of this development, the strategies described in this study are now more beneficial. Additionally, there has been an increase in the number of people who express their views in demeaning ways to others. As a result, hate speech has piqued interest in the subject of sentiment analysis, which has developed various algorithms for detecting emotions in social networks using intuitive means. This paper proposes the deep learning model to classify the sentiments in two separate analyses. In the first analysis, the tweets are classified based on the hate speech against the migrants and the women. In the second analysis, the detection is performed using a deep learning model to organise whether the hate speech is performed by a single or a group of users. During the text analysis, word embedding is implemented using the combination of deep learning models such as BiLSTM, CNN, and MLP. These models are integrated with word embedding methods such as inverse glove (global vector), document frequency (TF-IDF), and transformer-based embedding.

Cultural Dimensions in Twitter: Time, Individualism and Power

Previous studies have established the link between one's actions (e.g., engaging with others vs. minding one's own business) and one's national culture (e.g., collectivist vs. individualistic), and such actions have been shown to be important as they are collectively affiliated with a country's economic outcomes (e.g., Gross Domestic Product). Hitherto there has not been any systematic study of whether one's action on Twitter (e.g., deciding when to post messages) is linked to one's culture (e.g., country's Pace of Life). To fix that, we build different network snapshots starting from 55,000 seed users on Twitter, and we do so for 10 weeks across 30 countries (after filtering those with low penetration rates) for a total of 2.34 M profiles. Based on Hofstede's theory of cultural dimensions and Levine's Pace of Life theory, we consider three behavioral patterns on Twitter (i.e., temporal predictability of tweets, engaging with others, and supporting others who are less popular) and associate them with three different dimensions derived from the two theories: Pace of Life, Individualism and Power Distance. We find the following strong correlations: activity predictability negatively correlates with Pace of Life (r=-0.62), tweets with mentions negatively correlates with Individualism (r = -0.55), and power (e.g, Twitter popularity) imbalance in relationships (between, for example, two users mentioning each other) is correlated with Power Distance (r=0.62). These three cultural dimensions matter because they are associated with a country's socio-economic aspects - with GDP per capita, income inequality, and education expenditure.

Characterizing Information Diets of Social Media Users

With the widespread adoption of social media sites like Twitter and Facebook, there has been a shift in the way information is produced and consumed. Earlier, the only producers of information were traditional news organizations, which broadcast the same carefully-edited information to all consumers over mass media channels. Whereas, now, in online social media, any user can be a producer of information, and every user selects which other users she connects to, thereby choosing the information she consumes. Moreover, the personalized recommendations that most social media sites provide also contribute towards the information consumed by individual users. In this work, we define a concept of information diet — which is the topical distribution of a given set of information items (e.g., tweets) — to characterize the information produced and consumed by various types of users in the popular Twitter social media. At a high level, we find that (i) popular users mostly produce very specialized diets focusing on only a few topics; in fact, news organizations (e.g., NYTimes) produce much more focused diets on social media as compared to their mass media diets, (ii) most users' consumption diets are primarily focused towards one or two topics of their interest, and (iii) the personalized recommendations provided by Twitter help to mitigate some of the topical imbalances in the users' consumption diets, by adding information on diverse topics apart from the users' primary topics of interest.

A social network analysis of the organizations focusing on tuberculosis, malaria and pneumonia

In this paper,we present an original study on the use of social media data to analyze the structure of the global health networks (GHNs) relative to health organizations targeted to malaria, tuberculosis (TBC) and pneumonia as well as twitter popularity, evaluating the performance of their strategies in response to the arising health threats.We use a machine learning ensemble classifier and social network analysis to discover the Twitter users that represent organizations or groups active for each disease. We have found evidence that the GHN of TBC is the more mature, active and global. Meanwhile, the networks of malaria and pneumonia are found to be less connected and lacking global coverage.Our analysis validates the use of social media to analyze GHNs and to propose these networks as an important organizational tool in mobilizing the community versus global sustainable development goals.

Coastal and marine topics and destinations during the COVID-19 pandemic in Twitter's tourism hashtags

Coastal and marine tourism offer various topics discussed within social media communication during a pandemic. The present study analysed the discussion topics in the popular Twitter's tourism hashtags during the COVID-19 crisis related to coastal and marine tourism. The objectives were to (i) determine the discussion topics, (ii) identify the discussion topics of greater hierarchy, and (iii) determine the tourist destinations discussed. The data were collected from Twitter between March and April 2020. A total of 269,722 tweets were extracted in different languages and globally through the Twitter API of popular tourism hashtags about the COVID-19 pandemic using big data software. The word associations technique was used to process the Twitter data. The discussion topics were ‘travel’, ‘beach’, ‘cruise’, ‘sea’, ‘ocean’, ‘nature’, ‘sun’, ‘coast’, and others. The discussion topics of greatest hierarchy were ‘travel’, ‘beach’, ‘sea’, and ‘cruise’. These topics refer to the motivational dimensions‘ ‘escape’, and ‘sun and beach’. The geographic locations discussed were Spain, Canary, Hawaii, Australia, Caribbean, Bermuda, Florida, California, and others. The findings will contribute to the relationship of the topics with the tourist motivational dimensions, and the destinations discussed about marine and coastal tourism in the time of crisis in the COVID-2019 Pandemic in Twitter's tourism hashtags, and companies will also be able to improve their communication strategies and develop post-pandemic products.

Social Network Analysis and Visualization of Arabic Tweets During the COVID-19 Pandemic

Twitter data have been used widely in times of pandemics and crises. Currently, the world is suffering from the outbreak of new coronavirus disease 2019 (COVID-19), in which the COVID-19 virus has infected more than one hundred and twenty million people worldwide with more than two million deaths. Consequently, people tend to share COVID-19-related content on social media extensively. As a new pandemic, only a small number of studies have been conducted to analyze COVID-19-related tweets, and even fewer were meant for Arabic tweets. This research explores the influence of the COVID-19 pandemic on Saudi users’ tweeting behavior. In particular, the research adopts a social network analysis (SNA) for COVID-19 Arabic tweets. This approach is interesting, as it is based on analysis of social structures, such as Twitter users and the relationships among them, through the use of networks and graph theory without the contents of the tweets themselves. Based on 8905 collected Arabic tweets, this research resulted in three main contributions: 1) a visualization of the social network for COVID-19 tweets of Saudi users, 2) an identification of information sources that Twitter users employ during the COVID-19 pandemic, and 3) an identification of the most popular influencers among users of COVID-19 tweets. The results of this study may help identify the most popular Twitter influencers and those who deliver the information to Twitter users, utilizing them to increase awareness and deliver information and instructions to overcome the COVID-19 pandemic.

Surveilling COVID-19 Emotional Contagion on Twitter by Sentiment Analysis.

The fight against the COVID-19 pandemic seems to encompass a social media debate, possibly resulting in emotional contagion and the need for novel surveillance approaches. In the current study, we aimed to examine the flow and content of tweets, exploring the role of COVID-19 key events on the popular Twitter platform. Using representative freely available data, we performed a focused, social media-based analysis to capture COVID-19 discussions on Twitter, considering sentiment and longitudinal trends between January 19 and March 3, 2020. Different populations of users were considered. Core discussions were explored measuring tweets' sentiment, by both computing a polarity compound score with 95% Confidence Interval and using a transformer-based model, pretrained on a large corpus of COVID-19-related Tweets. Context-dependent meaning and emotion-specific features were considered. We gathered 3,308,476 tweets written in English. Since the first World Health Organization report (January 21), negative sentiment proportion of tweets gradually increased as expected, with amplifications following key events. Sentiment scores were increasingly negative among most active users. Tweets content and flow revealed an ongoing scenario in which the global emergency seems difficult to be emotionally managed, as shown by sentiment trajectories. Integrating social media like Twitter as essential surveillance tools in the management of the pandemic and its waves might actually represent a novel preventive approach to hinder emotional contagion, disseminating reliable information and nurturing trust. There is the need to monitor and sustain healthy behaviors as well as community supports also via social media-based preventive interventions.

El discurso pseudopolítico de la segunda pantalla. #ElDebateEnRTVE visto a través de sus prosumers

Introducción: Nuestro objeto de análisis es la conversación mantenida en Twitter en uno de los debates electorales de las elecciones generales del 28 de abril: el que tuvo lugar el 22 de abril en Televisión Española entre los candidatos de los cuatro principales partidos políticos: PP, PSOE, UP y Cs. Metodología: La metodología empleada en el trabajo es el análisis del discurso de base pragmática, en la que se emplean técnicas cuantitativas y cualitativas de análisis. Para este, se seleccionaron de forma aleatoria 1000 tuits de entre los más populares que habían empleado los hashtags promocionales del debate: #ElDebateEnRTVE y #ELDEBATEenRTVE. Resultados: Los resultados indican que los cuatro partidos analizados participaron en el debate paralelo generado en Twitter y que, si bien la mayor parte de las intervenciones en la segunda pantalla fueron de ataque, los partidos prefirieron sobre todo elogiar a su candidato, a excepción de Ciudadanos, que optó por el ataque al PSOE y luego por el elogio propio. Discusión y conclusiones: En cuanto a los temas tratados fueron no tanto políticos sino pseudopolíticos. Los memes, mensajes irónicos y bromas ocuparon un espacio reducido en esta conversación transmediática.

Characteristics of Gun Advertisements on Social Media: Systematic Search and Content Analysis of Twitter and YouTube Posts.

BackgroundAlthough gun violence has been identified as a major public health concern, the scope and significance of internet gun advertising is not known.ObjectiveThis study aimed to quantify the characteristics of gun advertising on social media and to compare the reach of posts by manufacturers with those of influencers.MethodsUsing a systematic search, we created a database of recent and popular Twitter and YouTube posts made public by major firearm manufacturers and influencers. From our sample of social media posts, we reviewed the content of the posts on the basis of 19 different characteristics, such as type of gun, presence of women, and military or police references. Our content analysis summarized statistical differences in the information conveyed in posts to compare advertising approaches across social media platforms.ResultsSample posts revealed that firearm manufacturers use social media to attract audiences to websites that sell firearms: 14.1% (131/928; ±2.9) of Twitter posts, 53.6% (228/425; ±6.2) of YouTube videos, and 89.5% (214/239; ±5.1) of YouTube influencer videos link to websites that facilitate sales. Advertisements included women in efforts to market handguns and pistols for the purpose of protection: videos with women included protection themes 2.5 times more often than videos without women. Top manufacturers of domestic firearms received 98 million channel views, compared with 6.1 billion channel views received by the top 12 YouTube influencers.ConclusionsFirearm companies use social media as an advertising platform to connect viewers to websites that sell guns. Gun manufacturers appropriate YouTube servers, video streaming services, and the work of YouTube influencers to reach large audiences to promote the widespread sale of consumer firearms. YouTube and Twitter subsidize gun advertising by offering server and streaming services at no cost to gun manufacturers, to the commercial benefit of Google and Twitter’s corporate ownership.

Lesbians’ Use of Popular Social Media Sites is Associated with Perceived Drinking Norms & Interest in Receiving Personalized Normative Feedback on Alcohol Use

ABSTRACT Alcohol-related problems disproportionately impact sexual minority women. Recent research suggests that lesbian-identified women overestimate peer drinking norms and therefore, personalized normative feedback (PNF) may be an appropriate and efficacious intervention strategy for reducing alcohol-related risks in this population. To inform the development and packaging of such interventions, this study examines lesbians’ use of the popular social media sites Facebook, Instagram, Snapchat, and Twitter in relation to alcohol consumption, perceptions of peer drinking norms, confidence in normative perceptions, and interest in receiving alcohol PNF. Findings suggest that the lesbians who are most likely to benefit from a PNF alcohol intervention may also be the most readily reached via social media sites. Alcohol interventions that both recruit and deliver PNF via widely used social media platforms such as Facebook and Instagram represent a promising and cost-effective strategy to reduce heavy drinking among lesbians.

PERAN TWITTER DALAM DIGITAL CUSTOMER RELATION MANAGEMENT DI INDUSTRI PERBANKAN

In a digital transformation era, various companies are competing to change communication patterns with consumers, so that consumers are expected to be more loyal to the company. One transformation is social media optimization to build a conversation with consumers. The use of Twitter as one of the social media platforms in Indonesia by the company is currently considered as something that is needed, even though Twitter's popularity is not as high as Facebook, Instagram, or Youtube. With 22 million users in Indonesia, Twitter is one channel for companies in customer relations, one of which is as part of the customer relationship management channel. Using the netnographic method, this study analyzes the role of Twitter as part of a company's CRM channel in the digital age, especially in the banking industry, as well as its implications for consumers. To cite this article (7 th APA style): Setyadi, D. K. (2019). Peran Twitter dalam Digital Customer Relationship Management di Industri Perbankan [The Role of Twitter in the Digital Customer Relationship Management in Banking Industry]. Journal Communication Spectrum, 9 (2), 110-124. http://dx.doi.org/10.36782/jcs.v9i2.1952

Liquidity Risk and Investors’ Mood: Linking the Financial Market Liquidity to Sentiment Analysis through Twitter in the S&amp;P500 Index

Microblogging services can enrich the information investors use to make financial decisions on the stock markets. As liquidity has immediate consequences for a trader’s movements, this risk is an attractive area of interest for both academics and those who participate in the financial markets. This paper focuses on market liquidity and studies the impact on liquidity and trading costs of the popular Twitter microblogging service. Sentiment analysis extracted from Twitter and different popular liquidity measures were gathered to analyze the relationship between liquidity and investors’ opinions. The results, based on the analysis of the S&amp;P 500 Index, found that the investors’ mood had little influence on the spread of the index.

Disrupting the neoliberal university in South Africa: The #FeesMustFall movement in 2015

This article analyses the 2015 student mobilizations in South Africa (SA), which arose in opposition to a 10% hike in tuition fees planned for 2016 at the University of Witwatersrand (WITS) and spurred a massive student reaction across all the universities of the country. After only 10 days of mobilization, the protest, also known as #FeesMustFall by virtue of the most popular Twitter hashtag associated with it, succeeded in halting the hike. How and why did the protesters win? To answer this question, this study combined various qualitative methods of analysis. The author carried out in-depth interviews with all the relevant actors involved in the issue, and analysed documents relating to the movement elaborated by the students in the year of the protest (2015), as well as the main policy documents on higher education in post-apartheid South Africa (1994–2016) released by the government. The author argues that massive and disruptive student protests play a crucial role in ‘young’ democracies, as is the case of today’s South Africa, in which higher education is still considered an important societal issue, and university-level students a legitimate political actor. Where students are perceived as a legitimate element of the political system, it is more likely for them to have an impact on society.