scholarly journals Latinx Digital Memory: Identity Making in Real Time

2019 ◽  
Vol 5 (4) ◽  
pp. 205630511986264
Author(s):  
Melissa Villa-Nicholas
Keyword(s):  
Social Media ◽  
Identity Formation ◽  
Cultural Values ◽  
Real Time ◽  
Cultural Memory ◽  
Corporate Sponsorship ◽  
Memory Work ◽  
Digital Memory ◽  
Social Media Platforms

Recently, there has been an increase in cultural Latinx social media platforms, leading to a renaissance of digital visual cultural memory work around Latinidad. This work focuses on how Latinx digital memory participates in an ongoing identity formation in real time, both in resistance to and collusion with American cultural values. By looking at popular visual Latinx social media accounts, this article explores how Latinx identity is constructed and adapted in real time, through Latinx social media platforms. Three main trends are noted as arising from Latinx social media: nostalgia around Latinx identity, the corporate sponsorship of Latinx memory, and the resistance to hegemonic Latinidad narratives.

scholarly journals Classification Connection of Twitter Data using K-Means Clustering

2019 ◽  
Vol 8 (6S4) ◽  
pp. 14-22
Keyword(s):  
Social Media ◽  
Real Time ◽  
Clustering Methods ◽  
Time Analysis ◽  
Data Set ◽  
Dynamic Data ◽  
Real Time Analysis ◽  
Twitter Data ◽  
Social Media Platforms ◽  
Insight Into

The rise of social media platforms like Twitter and the increasing adoption by people in order to stay connected provide a large source of data to perform analysis based on the various trends, events and even various personalities. Such analysis also provides insight into a person’s likes and inclinations in real time independent of the data size. Several techniques have been created to retrieve such data however the most efficient technique is clustering. This paper provides an overview of the algorithms of the various clustering methods as well as looking at their efficiency in determining trending information. The clustered data may be further classified by topics for real time analysis on a large dynamic data set. In this paper, data classification is performed and analyzed for flaws followed by another classification on the same data set.

scholarly journals Detecting Damage Building Using Real-Time Crowdsourced Images And Transfer Learning

2021 ◽  
Author(s):  
Gaurav Chachra ◽  
Qingkai Kong ◽  
Jim Huang ◽  
Srujay Korlakunta ◽  
Jennifer Grannen ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Learning Model ◽  
Research Community ◽  
Rescue Work ◽  
The Public ◽  
Social Media Platforms ◽  
Deep Learning Model

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

From fandom to fad: are millennials really engaged with and loyal to their loved brands on social media?

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Teresa Fernandes ◽  
Inês Inverneiro
Keyword(s):  
Social Media ◽  
Identity Formation ◽  
Brand Loyalty ◽  
Structural Equation ◽  
Holistic Model ◽  
Brand Love ◽  
Content Type ◽  
Brand Engagement ◽  
Social Media Platforms ◽  
Use Of Social Media

Purpose Exerting a peculiar fascination on both managers and academics, Millennials can be distinguished from other cohorts by their intense exposure to the internet and heavy use of social media, which, in turn, affect their identity formation, brand engagement, loyalty and purchase behaviour. Yet, uncertainties regarding online engagement and the real benefits brands can reap from Millennials’ avid use of social media remain. Therefore, by developing a holistic model of drivers and outcomes, this study aims to understand how Millennials engage with their most loved, self-expressive brands across social media platforms and its impact on loyalty-related intentions. Design/methodology/approach Data was gathered using a self-administered survey, answered by 343 millennial generation social media users and based on self-selected self-expressive, loved brands. Considering brand loyalty as a key outcome, a holistic model was developed and tested using partial least squares-structural equation modelling, emphasizing not only the role of social media engagement but also including brand love, experience and identification as direct and indirect antecedents. Findings Findings suggest a disconnection between online and offline brand relationships: though Millennials love and are very loyal to their favourite brands, they are not actively engaged in social media, which helps to explain the non-significant effect of engagement on brand loyalty. Moreover, together with brand identification, brand experience was found to play a major role in developing brand love, which, in turn, is positively related to engagement and loyalty. Originality/value Theoretically, this study contributes to bridging a gap in the literature, as research on engagement, its drivers and outcomes is scant and there is no robust evidence about its impact on brand loyalty, particularly among Millennials. Moreover, research on disengaged consumers who exhibit limited willingness to engage is still scant. Managerially, this study provides insights for brand managers wishing to successfully engage and build relationships with Millennials and to identify key routes to Millennials’ loyalty.

scholarly journals Let's Have a Tweetup: The Case for Using Twitter Professionally

2016 ◽  
Vol 140 (9) ◽  
pp. 956-957 ◽  
Author(s):  
Maren Y. Fuller ◽  
Timothy Craig Allen
Keyword(s):  
Social Media ◽  
Real Time ◽  
San Francisco ◽  
Media Use ◽  
Patient Privacy ◽  
Social Media Use ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
Social Media Network

Social media use is very common and can be an effective way for professionals to discuss information and interact with colleagues. Twitter (Twitter, Inc, San Francisco, California) is a social media network where posts, termed tweets, are limited to 140 characters. Professional use of Twitter is ideal for physicians interested in both networking and education and is optimally used to facilitate in-person networking. Live-tweeting (posting real-time reactions to events) at professional meetings is also a popular and highly successful use of Twitter. Physicians report patient privacy as the top concern preventing use of social media for professional reasons, and although generally social media use is safe, it is essential to understand how to protect patient confidentially. Other social media platforms with potential for professional use include Facebook (Facebook, Inc, Menlo Park, California), Instagram (Facebook, Inc), YouTube (YouTube, LLC, San Bruno, California), and Periscope (Twitter, Inc). With Twitter and other social media options, now is the time for pathologists to increase our visibility on social media and worldwide.

scholarly journals AthPPA: A Data Visualization Tool for Identifying Political Popularity over Twitter

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 312
Author(s):  
Alexandros Britzolakis ◽  
Haridimos Kondylakis ◽  
Nikolaos Papadakis
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Real Time ◽  
Data Visualization ◽  
Visualization Tool ◽  
Political Leaders ◽  
Political Event ◽  
Data Analyst ◽  
Industrial Sectors ◽  
Social Media Platforms

Sentiment Analysis is an actively growing field with demand in both scientific and industrial sectors. Political sentiment analysis is used when a data analyst wants to determine the opinion of different users on social media platforms regarding a politician or a political event. This paper presents Athena Political Popularity Analysis (AthPPA), a tool for identifying political popularity over Twitter. AthPPA is able to collect in-real-time tweets and for each tweet to extract metadata such as number of likes, retweets per tweet etc. Then it processes their text in order to calculate their overall sentiment. For the calculation of sentiment analysis, we have implemented a sentiment analyzer that is able to identify the grammatical issues of a sentence as well as a lexicon of negative and positive words designed specifically for political sentiment analysis. An analytic engine processes the collected data and provides different visualizations that provide additional insights on the collected data. We show how we applied our framework to the three most prominent Greek political leaders in Greece and present our findings there.

scholarly journals Social Media based Simulation Models for Understanding Disease Dynamics

2018 ◽  
Author(s):  
Ting Hua ◽  
Chandan K Reddy ◽  
Lei Zhang ◽  
Lijing Wang ◽  
Liang Zhao ◽  
...  
Keyword(s):  
Social Media ◽  
Real Time ◽  
Computational Models ◽  
Simulation Models ◽  
Population Level ◽  
Contact Network ◽  
Real World Data ◽  
Computational Epidemiology ◽  
Individual Health Status ◽  
Social Media Platforms

In this modern era, infectious diseases, such as H1N1, SARS, and Ebola, are spreading much faster than any time in history. Efficient approaches are therefore desired to monitor and track the diffusion of these deadly epidemics. Traditional computational epidemiology models are able to capture the disease spreading trends through contact network, however, one unable to provide timely updates via real-world data. In contrast, techniques focusing on emerging social media platforms can collect and monitor real-time disease data, but do not provide an understanding of the underlying dynamics of ailment propagation. To achieve efficient and accurate real-time disease prediction, the framework proposed in this paper combines the strength of social media mining and computational epidemiology. Specifically, individual health status is first learned from user's online posts through Bayesian inference, disease parameters are then extracted for the computational models at population-level, and the outputs of computational epidemiology model are inversely fed into social media data based models for further performance improvement. In various experiments, our proposed model outperforms current disease forecasting approaches with better accuracy and more stability.

scholarly journals #YELLOWPERILSUPPORTSBLACKPOWER: MEMORY WORK FOR INTERRACIAL SOLIDARITY

2021 ◽  
Author(s):  
Yena Kang
Keyword(s):  
Social Media ◽  
African Americans ◽  
Black Community ◽  
Asian Americans ◽  
Racial Justice ◽  
Digital Activism ◽  
Memory Work ◽  
The Social ◽  
Social Media Platforms ◽  
Justice Movements

As various racial justice movements emerged under the “Black Lives Matter” slogan after George Floyd’s murder in May 2020, Monyee Chau posted some artwork on Instagram with the slogan, #YellowPerilSupportsBlackPower. The artwork—symbolizing Asians with a yellow tiger and African Americans with a black panther—ignited Asians’ activism in support of African Americans and became circulated via multiple social media platforms. In this study, I view the #YellowPerilSupportsBlackPower movement (YPSBP) as digital activism, and I analyze how Asian Americans project their “Asianness” to advocate for the Black community. In particular, I focus on memory work among Asian participants when they demonstrate their solidarity with the Black community. By analyzing mediated memory work on Instagram, I identify the three types of memory work in which Asian participants engage. I conclude that this memory work plays a key role in legitimatizing a process through which Asian Americans can produce affective ties with the Black community that build a multiracial identity extending beyond color lines. This exploration of interracial solidarity enriches both the social movement and digital activism scholarship by illustrating how memory work mediates and amplifies affective solidarity.

Web 2.0 and News 2.0

2015 ◽  
pp. 93-110
Author(s):  
Kyle Gibson ◽  
Greg Gomer
Keyword(s):  
Social Media ◽  
Web 2.0 ◽  
Real Time ◽  
Distribution Models ◽  
Web Usage ◽  
News Source ◽  
News Organizations ◽  
Production And Distribution ◽  
Social Media Platforms ◽  
News Industry

This chapter examines the effects that Web 2.0 technologies have had on traditional news organizations and how those organizations have been forced to adapt their content style, speed of production, and distribution models. It specifically focuses on real-time analytics and how news organizations can utilize new opportunities presented by social media platforms and web usage mining to analyze their audience, the competition, and popular opinions. The chapter will explain in detail how a news organization can compile data from social media and web usage, gain insights from that data, and act upon those insights. To further examine real-time analytics, the chapter presents real examples from BostInno, an online news source, where real-time analytics affected content and distribution. To conclude, the authors will reflect on the impact real-time analytics has on the news industry and how it might affect it in the near future.

Real Time Sentiment Analysis

2020 ◽  
Vol 11 (1) ◽  
pp. 27-35
Author(s):  
Sandip Palit ◽  
Soumadip Ghosh
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Real Time ◽  
Unstructured Data ◽  
Societal Issues ◽  
The Social ◽  
The World ◽  
Social Media Platforms ◽  
Primary Medium ◽  
Different Parts

Data is the most valuable resource. We have a lot of unstructured data generated by the social media giants Twitter, Facebook, and Google. Unfortunately, analytics on unstructured data cannot be performed. As the availability of the internet became easier, people started using social media platforms as the primary medium for sharing their opinions. Every day, millions of opinions from different parts of the world are posted on Twitter. The primary goal of Twitter is to let people share their opinion with a big audience. So, if the authors can effectively analyse the tweets, valuable information can be gained. Storing these opinions in a structured manner and then using that to analyse people's reactions and perceptions about buying a product or a service is a very vital step for any corporate firm. Sentiment analysis aims to analyse and discover the sentiments behind opinions of various people on different subjects like commercial products, politics, and daily societal issues. This research has developed a model to determine the polarity of a keyword in real time.

Sign in / Sign up

Export Citation Format

Share Document