scholarly journals Archetype-Based Modeling and Search of Social Media

2019 ◽  
Vol 3 (3) ◽  
pp. 44
Author(s):  
Davis ◽  
Sedig ◽  
Lizotte
Keyword(s):  
Social Media ◽  
New Technique ◽  
Opioid Use ◽  
Social Media Data ◽  
Search Techniques ◽  
The Social ◽  
A New Technique ◽  
Media Data

Existing keyword-based search techniques suffer from limitations owing to unknown, mismatched, and obscure vocabulary. These challenges are particularly prevalent in social media, where slang, jargon, and memetics are abundant. We develop a new technique, Archetype-Based Modeling and Search, that can mitigate these challenges as they are encountered in social media. This technique learns to identify new relevant documents based on a specified set of archetypes from which both vocabulary and relevance information are extracted. We present a case study from the social media data from Reddit, by using authors from /r/Opiates to characterize discourse around opioid use and to find additional relevant authors on this topic.

Visual Social Media and Big Data. Interpreting Instagram Images Posted on Twitter

2016 ◽  
Vol 2 (2) ◽  
pp. 113-134 ◽  
Author(s):  
Dhiraj Murthy ◽  
Alexander Gross ◽  
Marisa McGarry
Keyword(s):  
Social Media ◽  
Image Data ◽  
Hurricane Sandy ◽  
Social Media Data ◽  
Production And Consumption ◽  
The Social ◽  
Application Programming ◽  
Programming Interfaces ◽  
Media Data

Abstract Social media such as Twitter and Instagram are fast, free, and multicast. These attributes make them particularly useful for crisis communication. However, the speed and volume also make them challenging to study. Historically, journalists controlled what/how images represented crises. Large volumes of social media can change the politics of representing disasters. However, methodologically, it is challenging to study visual social media data. Specifically, the process is usually labour-intensive, using human coding of images to discern themes and subjects. For this reason, Studies investigating social media during crises tend to examine text. In addition, application programming interfaces (APIs) for visual social media services such as Instagram and Snapchat are restrictive or even non-existent. Our work uses images posted by Instagram users on Twitter during Hurricane Sandy as a case study. This particular case is unique as it is perhaps the first US disaster where Instagram played a key role in how victims experienced Sandy. It is also the last major US disaster to take place before Instagram images were removed from Twitter feeds. Our sample consists of 11,964 Instagram images embedded into tweets during a twoweek timeline surrounding Hurricane Sandy. We found that the production and consumption of selfies, food/drink, pets, and humorous macro images highlight possible changes in the politics of representing disasters - a potential turn from top-down understandings of disasters to bottom-up, citizen informed views. Ultimately, we argue that image data produced during crises has potential value in helping us understand the social experience of disasters, but studying these types of data presents theoretical and methodological challenges.

scholarly journals Using Social Media in Tourist Sentiment Analysis: A Case Study of Andalusia during the Covid-19 Pandemic

2021 ◽  
Vol 13 (7) ◽  
pp. 3836
Author(s):  
David Flores-Ruiz ◽  
Adolfo Elizondo-Salto ◽  
María de la O. Barroso-González
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Cost Savings ◽  
Mass Scale ◽  
Tourism Sector ◽  
The Social ◽  
Analytical Tools ◽  
Media Data

This paper explores the role of social media in tourist sentiment analysis. To do this, it describes previous studies that have carried out tourist sentiment analysis using social media data, before analyzing changes in tourists’ sentiments and behaviors during the COVID-19 pandemic. In the case study, which focuses on Andalusia, the changes experienced by the tourism sector in the southern Spanish region as a result of the COVID-19 pandemic are assessed using the Andalusian Tourism Situation Survey (ECTA). This information is then compared with data obtained from a sentiment analysis based on the social network Twitter. On the basis of this comparative analysis, the paper concludes that it is possible to identify and classify tourists’ perceptions using sentiment analysis on a mass scale with the help of statistical software (RStudio and Knime). The sentiment analysis using Twitter data correlates with and is supplemented by information from the ECTA survey, with both analyses showing that tourists placed greater value on safety and preferred to travel individually to nearby, less crowded destinations since the pandemic began. Of the two analytical tools, sentiment analysis can be carried out on social media on a continuous basis and offers cost savings.

scholarly journals Using social media data to map the areas most affected by ISIS in Syria

2020 ◽  
Vol 26 (1) ◽  
pp. 464-470
Author(s):  
Mohamad Hasan
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Geographical Information ◽  
Data Mapping ◽  
Islamic State ◽  
Social Media Data ◽  
The Social ◽  
Mapping Process ◽  
Data Source ◽  
Media Data

This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.

The Social Media Macroscope: A science gateway for research using social media data

2020 ◽  
Vol 111 ◽  
pp. 819-828 ◽  
Author(s):  
Joseph T. Yun ◽  
Nickolas Vance ◽  
Chen Wang ◽  
Luigi Marini ◽  
Joseph Troy ◽  
...  
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Science Gateway ◽  
The Social ◽  
Media Data

scholarly journals Review of Data Visualization for Social Media Postings

2018 ◽  
Vol 7 (4.38) ◽  
pp. 939
Author(s):  
Nur Atiqah Sia Abdullah ◽  
Hamizah Binti Anuar
Keyword(s):  
Social Media ◽  
Data Visualization ◽  
Line Graph ◽  
Data Types ◽  
Social Media Data ◽  
Data Analyst ◽  
The Social ◽  
Social Media Platforms ◽  
Types Of Information ◽  
Media Data

Facebook and Twitter are the most popular social media platforms among netizen. People are now more aggressive to express their opinions, perceptions, and emotions through social media platforms. These massive data provide great value for the data analyst to understand patterns and emotions related to a certain issue. Mining the data needs techniques and time, therefore data visualization becomes trending in representing these types of information. This paper aims to review data visualization studies that involved data from social media postings. Past literature used node-link diagram, node-link tree, directed graph, line graph, heatmap, and stream graph to represent the data collected from the social media platforms. An analysis by comparing the social media data types, representation, and data visualization techniques is carried out based on the previous studies. This paper critically discussed the comparison and provides a suggestion for the suitability of data visualization based on the type of social media data in hand.      

Social Media to Social Media Analytics

2019 ◽  
Vol 10 (2) ◽  
pp. 57-70 ◽  
Author(s):  
Vikas Kumar ◽  
Pooja Nanda
Keyword(s):  
Social Media ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Ethical Concerns ◽  
Ethical Implications ◽  
The Social ◽  
Social Media Platforms ◽  
The Individual ◽  
The Impact ◽  
Media Data

With the amplification of social media platforms, the importance of social media analytics has exponentially increased for many brands and organizations across the world. Tracking and analyzing the social media data has been contributing as a success parameter for such organizations, however, the data is being poorly harnessed. Therefore, the ethical implications of social media analytics need to be identified and explored for both the organizations and targeted users of social media data. The present work is an exploratory study to identify the various techno-ethical concerns of social media engagement, as well as social media analytics. The impact of these concerns on the individuals, organizations, and society as a whole are discussed. Ethical engagement for the most common social media platforms has been outlined with a number of specific examples to understand the prominent techno-ethical concerns. Both the individual and organizational perspectives have been taken into account to identify the implications of social media analytics.

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