Twitter Content Research Articles

Recognizing Musical Entities in User-generated Content

Recognizing Musical Entities is important for Music Information Retrieval (MIR) since it canimprove the performance of several tasks such as music recommendation, genre classification or artist similarity. However, most entity recognition systems in the music domain have concentrated on formal texts (e.g. artists’ biographies, encyclopedic articles, etc.), ignoring rich and noisy user-generated content. In this work, we present a novel method to recognize musicalentities in Twitter content generated by users following a classical music radio channel. Our approach takes advantage of both formal radio schedule and users’ tweets to improve entity recognition. We instantiate several machine learning algorithms to perform entity recognition combining task-specific and corpus-based features. We also show how to improve recognition results by jointly considering formal and user-generated content.

Using Big Data to Study the Impact of Mass Violence: Opportunities for the Traumatic Stress Field.

Studying the community impact of mass violence using a Big Data approach from social media data (e.g., Twitter) offers traumatic stress researchers an unprecedented opportunity to study and clarify theoretical assumptions using large-scale, observational, ecologically valid data. We describe challenges and benefits of working with Twitter data and briefly review studies that used Twitter data to explore community responses to mass violence. We then demonstrate the use of Twitter data to examine community responses to a specific event: the 2015 San Bernardino terrorist attack, in which 14 people were killed and 22 were wounded. In a 6-week time frame around this attack, we evaluated the time course of community-level negative emotion. We downloaded 1.16 million tweets, representing 25,894 users from San Bernardino, CA, and a matched control community, Stockton, CA. All tweets were coded in R using the Linguistic Inquiry and Word Count (LIWC) negative emotion dictionary. A piecewise regression technique with a discontinuity analysis was used to evaluate pre- and postevent trajectories of negative emotion across the study window. Controlling for within-user variability, negative emotion increased by 6.2%, β = .182, SE = .014, p < .001, in San Bernardino on the day of the attack and remained elevated for 5 days; no elevation was observed in Stockton. We discuss how data-driven text analytic techniques are useful for exploring Twitter content generated after collective traumas and describe challenges and opportunities accompanying analyses of social media data to understand the impact of mass violence on affected populations.

Twitter Talk and Twitter Sharing in Times of Crisis: Exploring Rhetorical Motive and Agenda-Setting in the Ray Rice Scandal

ABSTRACTSocial media continues to challenge many of the default assumptions agenda-setting. One particular arena of society that has witnessed the growth of nontraditional content producers has been the sports community, especially when moments of crisis involving broader cultural concerns emerge. This study examines the Twitter content shared and produced regarding the conversation that occurred immediately after TMZ video showing Ray Rice attacking his then-fiancée went viral. By attending to these users’ rhetorical purposes and agendamelding activities, we content that, far from safely agreeing with the dominant opinions in their respective social communities or merely being expressive, users debated with and challenged one another, even inviting disagreement.

The use of Māori words in National Science Challenge online discourse

ABSTRACT New Zealand English is well-known for its heavy borrowing of words from Māori. This lexical component, unique to New Zealand English alone has been studied intensely over the last 50 years, particularly in newspaper media. Current research suggests the use is still increasing today, primarily in Māori-related contexts. Here, we analyse a surprising and unexpected use of Māori loanwords in science digital discourse (neither genre being previously investigated), where we discover a strong presence of Māori borrowings in National Science Challenge website and Twitter content. Using corpus linguistics methods, we argue that the use of Māori loanwords in this genre functions as a national identity building tool, used by various authors to signal that the ‘challenges’ the country faces are uniquely New Zealand’s ‘challenges’.

An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning

The rise of social media has led to an increasing online cyber-war via hate and violent comments or speeches, and even slick videos that lead to the promotion of extremism and radicalization. An analysis to sense cyber-extreme content from microblogging sites, specifically Twitter, is a challenging, and an evolving research area since it poses several challenges owing short, noisy, context-dependent, and dynamic nature content. The related tweets were crawled using query words and then carefully labelled into two classes: Extreme (having two sub-classes: pro-Afghanistan government and pro-Taliban) and Neutral. An Exploratory Data Analysis (EDA) using Principal Component Analysis (PCA), was performed for tweets data (having Term Frequency—Inverse Document Frequency (TF-IDF) features) to reduce a high-dimensional data space into a low-dimensional (usually 2-D or 3-D) space. PCA-based visualization has shown better cluster separation between two classes (extreme and neutral), whereas cluster separation, within sub-classes of extreme class, was not clear. The paper also discusses the pros and cons of applying PCA as an EDA in the context of textual data that is usually represented by a high-dimensional feature set. Furthermore, the classification algorithms like naïve Bayes’, K Nearest Neighbors (KNN), random forest, Support Vector Machine (SVM) and ensemble classification methods (with bagging and boosting), etc., were applied with PCA-based reduced features and with a complete set of features (TF-IDF features extracted from n-gram terms in the tweets). The analysis has shown that an SVM demonstrated an average accuracy of 84% compared with other classification models. It is pertinent to mention that this is the novel reported research work in the context of Afghanistan war zone for Twitter content analysis using machine learning methods.

Analysis of People Response on Twitter Towards Tidal Wave Disaster in the Southern Coast of Yogyakarta Special Province (Case Studies: Parangtritis Beach, Bantul Regency)

In July 2018 the movement of the wind from Australia to the Indian Ocean gives the impact on season transition from rainy to dry season. As the result, the wave becomes so much higher than normal condition as it hits the coastal area as well as in the southern part of Yogyakarta Special Province where is directly bordered with the Indian Ocean. Some impacted areas are popular tourism spots like Parangtritis Beach. The wave wrecks several shops along the beach owned by the local people. The majority of damaged objects are semi-permanent buildings constructed by traditional bamboo and timber. Moreover the tourism activity has been warned due to the dangerous condition. The advancement of technology becomes one of popular issues including the increasing of online social media usage. Internet and gadgets such as smartphone are recently the part of people lifestyle. The nowadays people prefer to access anything online through their smartphone including to find the news on the website or social media such as Twitter. One of interested news is about disaster particularly in recognizable places as well as about tidal wave disaster in Parangtritis Beach. This study aims to investigate the advantages of Twitter contents related to the tidal wave in Parangtritis Beach on people response about the disaster and the beach. The analysis applies sentiment analysis theory. Furthermore the data being collected in this research is online from Twitter accounts that has divided into three phases of disaster (before tidal wave, during tidal wave, and after tidal wave).

Event Detection in Twitter Stream Using Weighted Dynamic Heartbeat Graph Approach [Application Notes

Tweets about everyday events are published on Twitter. Detecting such events is a challenging task due to the diverse and noisy contents of Twitter. In this paper, we propose a novel approach named Weighted Dynamic Heartbeat Graph (WDHG) to detect events from the Twitter stream. Once an event is detected in a Twitter stream, WDHG suppresses it in later stages, in order to detect new emerging events. This unique characteristic makes the proposed approach sensitive to capture emerging events efficiently. Experiments are performed on three real-life benchmark datasets: FA Cup Final 2012, Super Tuesday 2012, and the US Elections 2012. Results show considerable improvement over existing event detection methods in most cases.

Stock Exchange Frequent Topics @NYSE

The utilization of Twitter content to predict the market is necessary since the fundamental perception related to the activities of the market influenced by the opinion in the market space. Therefore this research is utilizing the tweet of @NYSE and compare the research finding @IDX_BEI the stock exchange office in Indonesia. There is 2069 tweet extracted from the period January until June 2018. Further analysis using the Exploratory Factor Analysis conducted, and show that @NYSE delivering more varieties of information related to the stock market. It is not only information related to the IPO, but also an opinion from the recognized economist and analyst. The findings show that Twitter could be improved for further utilization and reduce asymmetric information related to the market Keywords: @NYSE, Frequent Topics, Unstructured Dataset DOI : 10.7176/RJFA/10-16-10 Publication date : August 31 st 2019

Twitter Conversations and English News Media Reports on Poliomyelitis in Five Different Countries, January 2014 to April 2015.

Twitter and media coverage on poliomyelitis help maintain global support for its eradication. To test our hypothesis that themes of polio-related tweets and media articles would differ by location of interest (hashtag of country name mentioned in the tweet; country name mentioned in media articles) but would be similar to each other (tweets and media articles) for each location of interest. We retrospectively examined a 40% random sample of Twitter data containing the hashtag #polio from January 1, 2014, to April 30, 2015 (N = 79,333), from which we extracted 5 subcorpora each with a co-occurring hashtag #India (n = 5027), #Iraq (n = 1238), #Nigeria (n = 1364), #Pakistan (n = 11,427), and #Syria (n = 2952). We also retrieved and categorized 73 polio-related English-language news stories from within the same timeframe. We assessed the association between polio-related English news themes and the Twitter content. Descriptive analyses and unsupervised machine learning (latent Dirichlet allocation modeling) were conducted on the 5 Twitter subcorpora. The results of the latent Dirichlet allocation modeling on the specific subcorpora with country co-occurring hashtags showed significant differences between the 5 countries in terms of content. English mass media content focused largely on violence/conflicts and cases of polio, whereas social media focused on eradication and vaccination efforts along with celebrations. Contrary to our hypothesis, our evidence suggests Twitter content differs significantly from English mass media content. Evidence from our study helps inform media monitoring and communications surveillance during global public health crises, such as infectious disease outbreaks, as well as reactions to health promotion campaigns.

Mediatisation in Twitter: an exploratory analysis of the 2015 Spanish general election

ABSTRACT The mediatisation model in politics assumes that media conveys political messages between parties and citizenship, with the risk of promoting issues that frame the electoral content in terms of competition. These dynamics could distract from the debate of ideas and political policies. However, digital media like Twitter provide direct communication channels between parties, candidates and users. The present research explores Twitter content during an electoral campaign focused on the four issues proposed by Patterson (1980) to assess mediatisation: political, policy, campaign and personal (regarding the candidate). The goal of this research study is to evaluate the degree of mediatisation on Twitter using this typology. The research also evaluates the influence of the issue on retweet volume. The study’s basis was a 15.8 million-tweet corpus obtained during the 2015 Spanish General Election pre-campaign and campaign. This dataset was analysed using an automatic classification system. The results highlighted a predominance of policy issues during both the pre-campaign and campaign, except for the two televised debates, during which campaign issues were the most prevalent. On the election night, users commented much more on political issues. Finally, the kind of issue most likely to be retweeted was policy issues.

“Kang Pisang”: Personal Branding of the Jokowi’s Youngest Son in the Microblogging Media

Awareness in using the technology for building a self brand through personal branding can appear in anyone, whether from the public figures, nor from the society in general. The youngest son of Presiden Joko Widodo (Jokowi), Kae Sang Pangareb (Kae Sang) is known as an person who has a high level of awareness on her social media. This study examines trends in shaping the content of microblogging as a personal branding of Kae Sang. Studies conducted on the accounts twitter Kae Sang (@kaesangp) by looking at twitter account contents and personal branding process through microblogging. According to personal brand buildings theory by Peter Montoya, this research take 8 personal brand building category, specialization, leadership, personality, distinctiveness, visibility, unity, persistence, and good will. It can be conveyed that the personal branding of Kae Sang on twitter complies with the criteria of the forming of personal brandingKeyword : Personal Branding; Microblogging; Twitter

David vs. Goliath in the Digital Age: The Effect of Network Structure and Content on the Adoption of Cultural Products

Social media platforms have transformed the way with which communication among industry players and relevant audiences can influence adoption of a cultural product. Using Twitter conversations regarding eBooks sold on Amazon.com between 2014 and 2015, this paper studies how producers in the U.S. publishing industry benefit differentially from Twitter network structure and contents. We find that generalists such as the Big Five publishers gain advantage from controversial contents that appeal to the general public whereas peripheral specialists such as indie publishers benefit from network redundancy and consensus of opinion within niche communities. By finding how producers targeting different audiences benefit from online network structure and content, we explore different strategies with which respective players can leverage social media platforms and accrue competitive advantage.

Pedagogical Demonstration of Twitter Data Analysis: A Case Study of World AIDS Day, 2014

As a pedagogical demonstration of Twitter data analysis, a case study of HIV/AIDS-related tweets around World AIDS Day, 2014, was presented. This study examined if Twitter users from countries with various income levels responded differently to World AIDS Day. The performance of support vector machine (SVM) models as classifiers of relevant tweets was evaluated. A manual coding of 1,826 randomly sampled HIV/AIDS-related original tweets from November 30 through December 2, 2014 was completed. Logistic regression was applied to analyze the association between the World Bank-designated income level of users’ self-reported countries and Twitter contents. To identify the optimal SVM model, 1278 (70%) of the 1826 sampled tweets were randomly selected as the training set, and 548 (30%) served as the test set. Another 180 tweets were separately sampled and coded as the held-out dataset. Compared with tweets from low-income countries, tweets from the Organization for Economic Cooperation and Development countries had 60% lower odds to mention epidemiology (adjusted odds ratio, aOR = 0.404; 95% CI: 0.166, 0.981) and three times the odds to mention compassion/support (aOR = 3.080; 95% CI: 1.179, 8.047). Tweets from lower-middle-income countries had 79% lower odds than tweets from low-income countries to mention HIV-affected sub-populations (aOR = 0.213; 95% CI: 0.068, 0.664). The optimal SVM model was able to identify relevant tweets from the held-out dataset of 180 tweets with an accuracy (F1 score) of 0.72. This study demonstrated how students can be taught to analyze Twitter data using manual coding, regression models, and SVM models.

How did Ebola information spread on twitter: broadcasting or viral spreading?

BackgroundInformation and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. Health information could be transmitted from one to many (i.e. broadcasting) or from a chain of individual to individual (i.e. viral spreading). The aim of this study is to examine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages.MethodsOur data was purchased from GNIP. We obtained all Ebola-related tweets posted globally from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships. Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns.ResultsOn average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcasting was more pervasive than viral spreading. We found that influential users and hidden influential users triggered more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users.ConclusionsBroadcasting was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work beneficially with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger many retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion. However, challenges remain due to uncertain credibility of these hidden influential users.

#Peyronies: An Analysis of Online Twitter Discussion of Peyronie's Disease.

We quantitatively and qualitatively analyzed Twitter Peyronie's disease discussions using the hashtag #Peyronies. Symplur, a Twitter analytics service, was used to analyze Twitter activity, users and content for #Peyronies, the official hashtag for Peyronie's disease, between April 2013 and September 2018. Activity was measured by tweets per month and cumulative users per year. Users were classified based on geography, occupation and organizational affiliation. Content analysis was performed by retrieving information about Twitter engagement metrics including retweets, links, media, mentions, replies, frequently used words and hashtags. A total of 3,278 tweets and 767 users employing #Peyronies were identified. Most tweets (75%) were sent with links and contained media images (49%). The average ± SD number of #Peyronies tweets per month increased from 17.7 ± 8.5 in 2013 to 121.4 ± 52.2 in 2018 (p <0.001). The number of users increased from 77 to 767 during this period and users tweeted from 61 countries. Physicians comprised 46% of the top influencers. Common words in #Peyronies tweets were "disease," "men" and "andropeyronie." Popular associated hashtags were #menshealth, #peyronie and #men. Codification of tweet content revealed that tweets were focused on clinical care discussions or online patient support. Our analysis demonstrates that Peyronie's disease Twitter discussions are growing. Although discussion volume is smaller relative to other benign urological pathologies such as kidney stones, this study shows that Peyronie's disease discussions are present on Twitter and are attracting users. Twitter is a digital tool that influences awareness and perceptions of Peyronie's disease.

Do tweets create value? A multi-period analysis of Twitter use and content of tweets for manufacturing firms

In this research we enquire if adoption of Twitter by manufacturing firms creates any value for the firm. We conduct two studies to examine the relationship between Twitter related activities of manufacturing firms and the market reaction towards these firms. We collect a novel multi-period dataset and analyse the overall impact of adoption of Twitter on Tobin's Q by employing a propensity score matching and difference-in-difference research design. Our findings suggest that adoption of Twitter increases the value of the firm post adoption. We also conduct additional robustness check such as use of Industry Week data as a proxy of firm value and find our results to be consistent. We adopt a text mining-based approach and examine the communication environment of the manufacturing firms. We use the Latent Dirichlet Allocation (LDA) algorithm for short texts and identify six broad topics among tweets posted by firms. Our panel regression based analysis suggests that there is positive association between divulging product related information and Tobin's Q. Our research showcases the strong impact of use of Twitter and contributes to the nascent literature on firm generated content. It is likely to encourage managers of manufacturing firms to start actively using Twitter for sharing product related information on social media.

A Little Goes a Long Way: Serial Transmission of Twitter Content Associated with Hurricane Irma and Implications for Crisis Communication

Although numerous studies have attempted to analyze Twitter content related to crises and disasters, less is known about the prominence of serial transmission or the message attributes that drive retweeting. The current study examined 3.57 million tweets collected in the aftermath of Hurricane Irma. The findings suggest that informational tweets, those containing specific imperative language, and those containing graphic elements played a more prominent role in the conversation. Likelihood of serial transmission was not related to follower count or account verification. Implications for emergency management are discussed.

Differences in Emotional and Pain-Related Language in Tweets About Dentists and Medical Doctors: Text Analysis of Twitter Content.

BackgroundSocial media provides people with easy ways to communicate their attitudes and feelings to a wide audience. Many people, unfortunately, have negative associations and feelings about dental treatment due to former painful experiences. Previous research indicates that there might be a pervasive and negative occupational stereotype related to dentists and that this stereotype is expressed in many different venues, including movies and literature.ObjectiveThis study investigates the language used in relation to dentists and medical doctors on the social media platform Twitter. The purpose is to compare the professions in terms of the use of emotional and pain-related words, which might underlie and reflect the pervasive negative stereotype identified in relation to dentists. We hypothesized that (A) tweets about dentists will have more negative emotion-related words than those about medical doctors and (B) pain-related words occur more frequently in tweets about dentists than in those about medical doctors.MethodsTwitter content (“tweets”) about dentists and medical doctors was collected using the Twitter application program interface 140Dev over a 4-week period in 2015, scanning the search terms “dentist” and “doctor”. Word content of the selected tweets was analyzed using Linguistic Inquiry and Word Count software. The research hypotheses were investigated using nonparametric Wilcoxon-Mann-Whitney tests.ResultsOver 2.3 million tweets were collected in total, of which about one-third contained the word “dentist” and about two-thirds contained the word “doctor.” Hypothesis A was supported since a higher proportion of negative words was used in tweets about dentists than in those about medical doctors (z=−10.47; P<.001). Similarly, tests showed a difference in the proportions of anger words (z=−12.54; P<.001), anxiety words (z=−6.96; P<.001), and sadness words (z=−9.58; P<.001), with higher proportions of these words in tweets about dentists than in those about doctors. Also, Hypothesis B was supported since a higher proportion of pain-related words was used in tweets about dentists than in those about doctors (z=−8.02; P<.001).ConclusionsThe results from this study suggest that stereotypes regarding dentists and dental treatment are spread through social media such as Twitter and that social media also might represent an avenue for improving messaging and disseminating more positive attitudes toward dentists and dental treatment.

#Sad: Twitter Content Predicts Changes in Cognitive Vulnerability and Depressive Symptoms

Research shows that social media networks can affect both the physical and mental health of its users. We hypothesized that social media would also be associated with cognitive vulnerability to depression. To test this hypothesis, we used a 3-month pre-post prospective longitudinal design with a sample of undergraduates (n = 105). Results showed that participants who had tweets with a “past focus” (as determined by LIWC software) were more likely to exhibit increases in cognitive vulnerability and depressive symptoms than participants who did not have tweets with a past focus. Increases in cognitive vulnerability were associated with increases in depressive symptoms. However, the effect of Twitter content on future depressive symptoms was not accounted for by increases in cognitive vulnerability. Rather, one’s past focus Twitter content had an effect on future depressive symptoms that was independent of its effect on future cognitive vulnerability levels. These results provide further support for the plasticity of cognitive vulnerability in early adulthood as well as corroborate emerging evidence for the association between social media and mental health risk factors.

Big Data Analysis of Volatility Spillovers of Brands across Social Media and Stock Markets

Volatility is an important metric of financial performance, indicating uncertainty or risk. So, predicting and managing volatility is of interest to both company managers and investors. This study investigates whether volatility in user-generated content (UGC) can spill over to volatility in stock returns and vice versa. Sources for user-generated content include tweets, blog posts, and Google searches. The authors test the presence of these spillover effects by a multivariate GARCH model. Further, the authors use multivariate regressions to reveal which type of company-related events increase volatility in user-generated content.Results for two studies in different markets show significant volatility spillovers between the growth rates of user-generated content and stock returns. Further, specific marketing events drive the volatility in user-generated content. In particular, new product launches significantly increase the volatility in the growth rates of user-generated content, which in turn can spill over to volatility in stock returns. Moreover, the spillover effects differ in sign depending on the valence of the user-generated content in Twitter. The authors discuss the managerial implications.