Twitter Profiles Research Articles

The Aesthetics of Dehumanization: Anti-Roma Narratives in Italian Far-Right Propaganda, 2017–2022

Abstract: Beginning in the early 2010s, Italy has seen a new wave of popularization of far-right populist forces in institutional political contexts. Parties such as the Lega (League) and Fratelli d'Italia (Brothers of Italy) have engaged in campaigns of legitimation of ideologies based on ethnonationalist assumptions and racial exclusion that have become increasingly normalized within the public debate. This article analyzes the implementation of dehumanizing aesthetics in the propaganda of today's Italian far-right movements as applied to the discrimination of Roma minorities. Informed by old patterns of prejudice, the general storytelling around Roma in Italy is framed within fearful imaginaries of threat exploited by far-right groups to legitimize racist discourses and promote exclusionary policy agendas. The discussion considers the ability of far-right movements to use popular social media platforms for the diffusion of dehumanizing narratives that attempt to influence people's perception of Roma in public discourse. This article dissects some of the main elements informing anti-Roma narratives in the Italian political scene by considering a selection of case studies drawn from Facebook and Twitter profiles of institutional Italian far-right leaders. The discussion explores the connection between online and offline dimensions by examining the events at Torre Maura, a suburban borough of Rome that in 2019 saw violent anti-Roma protests led by neofascist fringe groups. This example is deemed interesting for the analysis of the potential of far-right aesthetics to transcend the online dimension and turn a general climate of fear, hate, and social tension into offline actions and behaviors.

My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles

New mapping and location applications focus on offering improved usability and services based on multi-modal door to door passenger experiences. This helps citizens develop greater confidence in and adherence to multi-modal transport services. These applications adapt to the needs of the user during their journey through the data, statistics and trends extracted from their previous uses of the application. The My-Trac application is dedicated to the research and development of these user-centered services to improve the multi-modal experience using various techniques. Among these techniques are preference extraction systems, which extract user information from social networks, such as Twitter. In this article, we present a system that allows to develop a profile of the preferences of each user, on the basis of the tweets published on their Twitter account. The system extracts the tweets from the profile and analyzes them using the proposed algorithms and returns the result in a document containing the categories and the degree of affinity that the user has with each category. In this way, the My-Trac application includes a recommender system where the user receives preference-based suggestions about activities or services on the route to be taken.

Automatic gender detection in Twitter profiles for health-related cohort studies.

ObjectiveBiomedical research involving social media data is gradually moving from population-level to targeted, cohort-level data analysis. Though crucial for biomedical studies, social media user’s demographic information (eg, gender) is often not explicitly known from profiles. Here, we present an automatic gender classification system for social media and we illustrate how gender information can be incorporated into a social media-based health-related study.Materials and MethodsWe used a large Twitter dataset composed of public, gender-labeled users (Dataset-1) for training and evaluating the gender detection pipeline. We experimented with machine learning algorithms including support vector machines (SVMs) and deep-learning models, and public packages including M3. We considered users’ information including profile and tweets for classification. We also developed a meta-classifier ensemble that strategically uses the predicted scores from the classifiers. We then applied the best-performing pipeline to Twitter users who have self-reported nonmedical use of prescription medications (Dataset-2) to assess the system’s utility.Results and DiscussionWe collected 67 181 and 176 683 users for Dataset-1 and Dataset-2, respectively. A meta-classifier involving SVM and M3 performed the best (Dataset-1 accuracy: 94.4% [95% confidence interval: 94.0–94.8%]; Dataset-2: 94.4% [95% confidence interval: 92.0–96.6%]). Including automatically classified information in the analyses of Dataset-2 revealed gender-specific trends—proportions of females closely resemble data from the National Survey of Drug Use and Health 2018 (tranquilizers: 0.50 vs 0.50; stimulants: 0.50 vs 0.45), and the overdose Emergency Room Visit due to Opioids by Nationwide Emergency Department Sample (pain relievers: 0.38 vs 0.37).ConclusionOur publicly available, automated gender detection pipeline may aid cohort-specific social media data analyses (https://bitbucket.org/sarkerlab/gender-detection-for-public).

Masters of Wine on Twitter: presence, activity, impact and community structure

Globalisation, the Internet and social media have changed the kind of actors with influence in the wine industry and the way these actors create signals to communicate credible information about experience and trust attributes. Among the most prestigious experts in the world of wine are the Masters of Wine (MW). Although initially devoted to international trade, they have spread their activities and their opinion is more and more appreciated by producers and consumers. The main objective of this article is to determine this community of experts’ behaviour on Twitter. In order to do so, four factors (presence, activity, impact and community) have been considered. All Twitter profiles belonging to users awarded with the MW qualification were identified and analysed. In addition, a set of 35,653 tweets published by the MWs were retrieved and analysed through descriptive statistics. The results show MWs on Twitter as high attractors (number of followers), moderate publishers (original contents published), moderate influencers (number of likes and retweets), and low interactors (number of friends and mentions to other users). These findings reveal that the MW community is not using Twitter to gain or reinforce their reputation as an accredited expert in the wine industry, giving more influential space on Twitter to consumers and amateurs.

Multiobjective fuzzy vehicle routing using Twitter data: Reimagining the delivery of essential goods

The world faced a major disruption in the form of the coronavirus disease (COVID-19) pandemic, which caused many countries to impose severe restrictions on movement, popularly known as “lockdown.” These lockdowns impacted transportation adversely, leading to massive disruptions in global and local supply chains. As the local markets were shut down, more people started turning to e-commerce logistics platforms offering doorstep deliveries of essential items (food and medicines). This resulted in an explosion in demand for such services, and businesses struggled to complete their deliveries. Additionally, the volume of real-time text data suddenly increased, as these customers started sharing their feedback on social media platforms. The availability of real-time raw text data and its popularity for solving complex business problems motivated the development of the approach proposed herein to address last-mile delivery issues. Thus, this paper suggests the use of Twitter data to identify the various grievances of customers about e-commerce logistics platforms. Natural language processing, a popular tool for text analytics, is employed to extract consumer tweets from the Twitter profiles of such businesses and subsequently to clean, process, and analyse them. Issues are categorized and used as objectives in a multiobjective fuzzy vehicle routing problem (VRP). An integrated hybrid fuzzy VRP is developed and coded to solve last-mile delivery issues. Experimental results and comparative analyses highlight the benefits of the novel approach. Managerial insights and scope for future research assist in the further development of the idea.

COVID-19 Vaccine Hesitancy in Canada: Content Analysis of Tweets Using the Theoretical Domains Framework.

BackgroundWith the approval of two COVID-19 vaccines in Canada, many people feel a sense of relief, as hope is on the horizon. However, only about 75% of people in Canada plan to receive one of the vaccines.ObjectiveThe purpose of this study is to determine the reasons why people in Canada feel hesitant toward receiving a COVID-19 vaccine.MethodsWe screened 3915 tweets from public Twitter profiles in Canada by using the search words “vaccine” and “COVID.” The tweets that met the inclusion criteria (ie, those about COVID-19 vaccine hesitancy) were coded via content analysis. Codes were then organized into themes and interpreted by using the Theoretical Domains Framework.ResultsOverall, 605 tweets were identified as those about COVID-19 vaccine hesitancy. Vaccine hesitancy stemmed from the following themes: concerns over safety, suspicion about political or economic forces driving the COVID-19 pandemic or vaccine development, a lack of knowledge about the vaccine, antivaccine or confusing messages from authority figures, and a lack of legal liability from vaccine companies. This study also examined mistrust toward the medical industry not due to hesitancy, but due to the legacy of communities marginalized by health care institutions. These themes were categorized into the following five Theoretical Domains Framework constructs: knowledge, beliefs about consequences, environmental context and resources, social influence, and emotion.ConclusionsWith the World Health Organization stating that one of the worst threats to global health is vaccine hesitancy, it is important to have a comprehensive understanding of the reasons behind this reluctance. By using a behavioral science framework, this study adds to the emerging knowledge about vaccine hesitancy in relation to COVID-19 vaccines by analyzing public discourse in tweets in real time. Health care leaders and clinicians may use this knowledge to develop public health interventions that are responsive to the concerns of people who are hesitant to receive vaccines.

CYBERGUARD: A Mobile Cyberbullying Detector

Social media is something that is used by a lot of people in Malaysia, especially among youths today. Social media can come in many different platforms such as facebook and twitter. However, due to the convenience given, a person can send a message to another person through social media in the blink of an eye. Many cases of bullying occur online, and these cases are known as cyber bullying. Cyber bullying happens when a person bullies and harasses another user online by sending harassing messages such as messages with rude words or messages with sexual harassment elements. Cyber bullying can be dangerous, affecting a person's emotion, especially among youths, because they have no experience with cyber bullying and doesn't know how to overcome this problem. As such, an android-based application named cyber guard has been developed to overcome this problem. This application is developed by connecting the user's twitter profile to retrieve the messages from the user's twitter messages. This application will alert the user if the application detects a negative implication's word retrieved from their twitter account. This application will also explain the meaning of the negative implication's word and their threat level. For users experiencing cyber bullying, the application provides advice and ways to overcome cyber bullying issues. Fifteen users have tested this application and the application has received good feedbacks from the users. This application is vital to everyone, especially youths, to know whether they are being cyber bullied and the steps that can be taken to overcome it.

Comparative analysis of the broadcaster’s Twitter strategies of the highest-rated British and Spanish TV series

The current interrelation between social media and the audio-visual industry has required traditional broadcasters to extend their programming strategies outside the box. TV fiction is a suitable genre to test new forms of audience loyalty, which is essential in a highly competitive environment. In this article, we present results regarding the actions of the industry through a content analysis of the official Twitter profiles of the most-watched series in Spain and the United Kingdom. We conclude that both market strategies revolve around the linear broadcast. However, Spanish channels are more aggressive in their actions to stimulate social conversation.

There Is No Nil in NIL: Examining the Social Media Value of Student-Athletes’ Names, Images, and Likeness

This research contributes to our understanding of the name, image, and likeness (NIL) value of student-athletes’ social media profiles. In Study 1, we scraped data from the Twitter (Nf = 3,880; Nb =757) and Instagram (Nf = 2,223; Nb = 647) profiles of male collegiate-level football and basketball athletes. In Study 2, we scraped data from the Twitter (N =1,058; posts = 20,978) and Instagram (N = 1,410; posts = 16,453) profiles of all student-athletes of four (two top-tier and two mid-tier) representative universities. Applying influencer marketing industry standard rates, the current research shows that student-athletes possess NIL value. Results further indicate student-athletes’ NIL value has a relationship with, but is not solely generated at, the institution level. Thus, this research contributes to student-athlete personal branding knowledge and informs discussion around policy and industry practice related to student-athletes, university athletics departments, athletic conferences, and the National Collegiate Athletic Association (NCAA).

Sentiment Analysis of the Syrian Conflict on Twitter

Social media have become an important means of imposing ideas and interests in social‏ conflicts. The Syrian conflict is analysed using sentiment analysis of tweets in order to establish how the‏ sentiment shapes the modern political landscape and influences recipient knowledge. The importance of‏ social networks and their potential in overthrowing regimes as well as in radicalization are highlighted.‏ The authors suggest several stages that can be used for analysing tweets and how they impact the reader‏ with selected narration. Sentiment analysis is used on a trained data set as a way to gain insight into‏ tweets of different factions in the Syria conflict. Selected tweets on missile strikes were published on 14‏ April 2018 and the day after. The Twitter profiles of three different sides – pro-Assad, pro-West and anti-‏ Assad – were also analysed. The results show that there is a real battle on social media with the purpose‏ of influencing human emotions.

Male, Female, and Nonbinary Differences in UK Twitter Self-descriptions: A Fine-grained Systematic Exploration

Abstract Purpose Although gender identities influence how people present themselves on social media, previous studies have tested pre-specified dimensions of difference, potentially overlooking other differences and ignoring nonbinary users. Design/methodology/approach Word association thematic analysis was used to systematically check for fine-grained statistically significant gender differences in Twitter profile descriptions between 409,487 UK-based female, male, and nonbinary users in 2020. A series of statistical tests systematically identified 1,474 differences at the individual word level, and a follow up thematic analysis grouped these words into themes. Findings The results reflect offline variations in interests and in jobs. They also show differences in personal disclosures, as reflected by words, with females mentioning qualifications, relationships, pets, and illnesses much more, nonbinaries discussing sexuality more, and males declaring political and sports affiliations more. Other themes were internally imbalanced, including personal appearance (e.g. male: beardy; female: redhead), self-evaluations (e.g. male: legend; nonbinary: witch; female: feisty), and gender identity (e.g. male: dude; nonbinary: enby; female: queen). Research limitations The methods are affected by linguistic styles and probably under-report nonbinary differences. Practical implications The gender differences found may inform gender theory, and aid social web communicators and marketers. Originality/value The results show a much wider range of gender expression differences than previously acknowledged for any social media site.

The importance of social media to the academic surgical literature: Relationship between Twitter activity and readership metrics

BackgroundSocial media has an increasing role within professional surgical practice, including the publishing and engagement of academic literature. This study aims to analyze the relationship between social media use and traditional and alternative metrics among academic surgical journals. MethodJournals were identified through the InCites Journal Citation Reports 2019, and their impact factor, h-index, and CiteScore were noted. Social media platforms were examined, and Twitter activity interrogated between 1 January to 31 December 2019. Healthcare Social Graph score and an aggregated Altmetric Attention Score were also calculated for each journal. Statistical analysis was carried out to look at the correlation between traditional metrics, Twitter activity, and altmetrics. ResultsJournals with a higher impact factor were more likely to use a greater number of social media platforms (R2 = 0.648; P < .0001). Journals with dedicated Twitter profiles had a higher impact factor than journals without (median, 2.96 vs 1.88; Mann-Whitney U = 390; P < .001); however, over a 1-year period (2018–2019) having a Twitter presence did not alter impact factor (Mann-Whitney U = 744.5; P = .885). Increased Twitter activity was positively correlated with impact factor. Longitudinal analysis over 6 years suggested cumulative tweets correlated with an increased impact factor (R2 = 0.324, P = .004). Novel alternative measures including Healthcare Social Graph score (R2 = 0.472, P = .005) and Altmetric Attention Score (R2 = 0.779, P = .001) positively correlated with impact factor. ConclusionHigher impact factor is associated with social media presence and activity, particularly on Twitter, with long-term activity being of particular importance. Modern alternative metrics correlate with impact factor. This relationship is complex, and future studies should look to understand this further.

“We Have Taken a Major Step Forward Today”: The Use of Twitter by Spanish Minor Parties

ABSTRACT Minor parties – also known as “third parties” in the US – are integral components of democratic systems. Despite being ignored by the mainstream media, contemporary Spanish politics reveal that minor parties may challenge the hegemony of traditional organizations, and even enter parliamentary politics. This paper analyzes Spanish minor parties’ social media communication in the context of the April 2019 election. A sample of 1,498 tweets was content-analyzed, gathered from the official Twitter profiles of the four main national minor parties: PACMA, the Communist Party of the Spanish Peoples, Zero Cuts, and VOX. Results indicate a lack of party-citizen interaction, as well as a meta-campaigning approach whereby the tweets’ topics and functions are mostly related to the parties themselves and campaign behavior.

Training on Introduction to Platforms and Utilization of Social Media in Online Business for MSME Actors Students of SMK Alwashliyah 2 Simalungun Regency

Given the blossoming essence of social media marketing, literature does not delve into the complexities and consequences of small and medium-sized enterprises (SMEs). This paper seeks to advance awareness and provide an extensive analysis of the inspiration and efficiency benefits of social media provided by small and medium-sized businesses through a digital economy analysis. The conceptual analysis shows that interactivity, compatibility, and cost-effectiveness are reasons for social media use, ultimately providing advantages. SMEs of Facebook and Twitter profiles are used to analyze the conceptual paradigm suggested. This research reveals that the interdependencies of social media's motives and the consequences of social media use are optimistic but sporadic across small and medium-sized companies. SMEs selling physical goods prefer to use social media on cost-effective motivations, whereas small and medium-sized businesses tend to see interactivity as the main incentive. Furthermore, the study results indicate that Facebook and Twitter in a two-site story generate success gains for small and medium-sized enterprises' users. The study offers future analysis and management priority problems that strongly affect the academic and practice culture. Obviously, despite any bottlenecks in use, SMEs in developing markets tend to reap expanded social media gains through multi-platform marketing activities.

Multi-contextual spammer detection for online social networks

With prevalent usage of internetworking, online social platforms have greatly revolutionized the traditional exchange of information. Social networking sites, including Facebook, Twitter, LinkedIn, etc., are being extensively used by the masses to create social as well as professional content and connections. However, due to wide transparency in information spread, the user profiles often remain vulnerable to spammers. Such threats occur in terms of harmful links being posted, involving eavesdropping attack and making fake identities of other people. The proposed model performs detection of spammers on the basis of real-time tweets extracted from Twitter. Several features have been taken into consideration while detecting the spammed Twitter profiles. The social network features include replies, mentions, web links, trending tags and profile reputation. Another feature has been introduced in the content-based feature to analyze sentiment of each tweet with consideration of the emoticons. Further, machine learning algorithms have been applied to conduct performance evaluation with respect to accuracy and spam accounts detection.

Digital Memory in the Post-Witness Era: How Holocaust Museums Use Social Media as New Memory Ecologies

With the passing of the last testimonies, Holocaust remembrance and Holocaust education progressively rely on digital technologies to engage people in immersive, simulative, and even counterfactual memories of the Holocaust. This preliminary study investigates how three prominent Holocaust museums use social media to enhance the general public’s knowledge and understanding of historical and remembrance events. A mixed-method approach based on a combination of social media analytics and latent semantic analysis was used to investigate the Facebook, Twitter, Instagram, and YouTube profiles of Yad Vashem, the United States Holocaust Memorial Museum, and the Auschwitz–Birkenau Memorial and Museum. This social media analysis adopted a combination of metrics and was focused on how these social media profiles engage the public at both the page-content and relational levels, while their communication strategies were analysed in terms of generated content, interactivity, and popularity. Latent semantic analysis was used to analyse the most frequently used hashtags and words to investigate what topics and phrases appear most often in the content posted by the three museums. Overall, the results show that the three organisations are more active on Twitter than on Facebook and Instagram, with the Auschwitz–Birkenau Museum and Memorial occupying a prominent position in Twitter discourse while Yad Vashem and the United States Holocaust Memorial Museum had stronger presences on YouTube. Although the United States Holocaust Memorial Museum exhibits some interactivity with its Facebook fan community, there is a general tendency to use social media as a one-way broadcast mode of communication. Finally, the analysis of terms and hashtags revealed the centrality of “Auschwitz” as a broad topic of Holocaust discourse, overshadowing other topics, especially those related to recent events.

JUUL the heartbreaker: Twitter analysis of cardiovascular health perceptions of vaping.

INTRODUCTIONThe public most frequently associates tobacco use solely with pulmonary health risks, despite heart disease being the leading cause of death in smokers. The health perceptions of e-cigarettes, especially cardiovascular health, have not been well studied. We aimed to evaluate the prevalence and health perceptions of tweets related to cardiovascular, pulmonary, and brain health – three organ systems for which tobacco use is a major disease risk factor.METHODSWe examined the cardiovascular, pulmonary, and brain health perceptions of vaping and JUUL on Twitter, followed by a content analysis of tweets pertaining to the cardiovascular risks. A Twitter firehose API scraped about 6.2 million publicly available tweets from 2015–2019 that contained vaping-related terms, and a separate dataset of about 1.9 million tweets that contained the term JUUL. A quantitative content analysis (n=2145) of tweets was subsequently conducted to assess the health perceptions of vaping and JUUL. Two trained coders independently assessed the posts and Twitter profiles to determine age (<18 or ≥18 years), sex, race, sentiment towards JUUL, and vaping-related topics.RESULTSThe majority of tweets containing vaping or JUUL-related terms did not also contain cardiovascular, pulmonary, or brain health terms (97.99% and 96.67%, respectively). Multiple linear regression analysis showed that youth (<18 years), females, non-White individuals, mention of a flavor, and mention of cardiovascular health harm words were associated with more positive sentiments towards JUUL. Pearson’s chi-squared analyses indicated that youth were more likely to mention a JUUL flavor. Females and youth were more likely to reference cardiovascular terms with humor.CONCLUSIONSThe cardiovascular health risks of vaping are not fully recognized by the public. Vulnerable populations such as youth and females reference JUUL with cardiovascular-related words that downplay the severity of tobacco as a major risk factor for cardiovascular disease.

Examining the Relationship between Altmetric Score and Traditional Bibliometrics in the Ophthalmology Literature for 2013 and 2016 Cohorts.

Background In this study, we reviewed a select sample of ophthalmology literature to determine if there was a correlation between Altimetric and traditional citation-based and impact factor metrics. We hypothesized that Altmetric score would more closely correlate with impact factor and citations in 2016. Methods Journal Citation Reports for the year 2013 was used to find the 15 highest impact factor ophthalmology journals in 2013. Then Elsevier's Scopus was used to identify the 10 most cited articles from each journal for the years 2013 and 2016. Metrics for all identified articles were collected using the Altmetric Bookmarklet, and date of Twitter account creation was noted for journals with such an account. Altmetric scores, impact factor, and citation counts were tabulated for each article. Pearson's correlation coefficient ( r ) determined correlation of independent variables (number of citations or impact factor) with dependent variable (Altmetric score). For our Twitter analysis, account age was the independent variable and calculated correlation coefficients ( r ) were the dependent variable. Proportion of variance was determined with a coefficient of determination ( R 2 ). Results This study included 300 articles, evenly split between 2013 and 2016. Within the 2013 cohort, three journals had significant positive correlations between citation count and Altmetric score. For the 2016 cohort, both Altmetric score and citation count ( r = 0.583, p < 0.001) and Altmetric score and impact factor ( r = 0.183, p = 0.025) revealed significant positive correlations. In 2016, two journals were found to have significant correlations between Altmetric score and citation number. Neither year revealed a significant correlation between the age of a journal's Twitter profile and the relationship between Altmetric score and citation count. In each year, Twitter accounted for the highest number of mentions. Conclusion The findings suggest that correlation between Altmetric score and traditional quality metric scores may be increasing. Altmetric score was correlated with impact factor and number of citations in 2016 but not 2013. At this time, Altmetrics are best used as an adjunct that is complementary but not an alternative to traditional bibliometrics for assessing academic productivity and impact.

Corporate Social Responsibility in the restaurant and fast food industry: a study of communication on healthy eating through social networks

The communication of Corporate Social Responsibility (CSR) of a sample of eight restaurant and fast food companies is analyzed. An exploratory and descriptive method, based on the content analysis technique, is used to analyze the formal and informal CSR communication channels used to promote healthy eating on their respective websites and Twitter profiles. This study is complemented by a survey (n = 383) applied to a sample of young Spaniards to investigate their interest in healthy eating information, their assessment of CSR actions carried out in this area as well as the communication channels used for this purpose. The results confirm that the CSR policies of the companies include actions aimed at guaranteeing and promoting healthy eating. It is concluded about the convenience of using their own social networks and influencers to encourage healthy eating habits more effectively in their publics.

Demographic profile building for cold start in recommender system: A social media fusion approach

New user recommendation is a big challenge for social networks site like reddit, where user profile is not so much rich. The aim of this paper is to enhance recommendation process that reddit can used for its users. We know that twitter and Facebook generate rich user information for social media behavior analysis and research. reddit recommendation system usually implement generic recommendation for its users in order to recommend subreddit, other side of coin, Like LinkedIn, amazon have very specialized user and item recommendation system based on user detail and create effective user profile for future reference. In this work fusion twitter and Facebook dataset user profile building by using tweets and post posted on Facebook by authentic unique user, and after that we extract user profile feature and parallelly perform subreddit listing for specific user feature generated by profile building. As per the reddit rank it is the 5th most visited site of USA and in world it holds 13th rank of most visited site. Twitter has 330 million active users with rich user profile like user demography. In this paper we analyze user timeline and identify user interest using machine learning and extract keywords using TF-IDF. Given the output with this machine learning used as input for a web based reddit subreddit listing application.