Tracking via Geotagged Social Media Data

2020 ◽  
Author(s):  
Anne Hardy
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Single Point ◽  
Application Programming Interface ◽  
Continuous Location ◽  
Social Media Data ◽  
Large Numbers ◽  
Application Programming ◽  
Media Data ◽  
Programming Interface

Over the past twenty years, social media has changed the ways in which we plan, travel and reflect on our travels. Tourists use social media while travelling to stay in touch with friends and family, enhance their social status (Guo et al., 2015); and assist others with decision making (Xiang and Gretzel, 2010; Yoo and Gretzel, 2010). They also use it to report back to their friends and family where they are. This can be done using a geotag function that provides a location for where a post is made. While little is known about why tourists choose to geotag their social media posts, Chung and Lee (2016) suggest that geotags may be used in an altruistic manner by tourists, in order to provide information, and because they elicit a sense of anticipated reward. What is known, however, is that the function offers researchers the ability to understand where tourists travel. There are two types of geotagged social media data. The first of these is discussed in this chapter and may be defined as single point geo-referenced data – geotagged social media posts whose release is chosen by the user. This includes data gathered from social media apps such as Facebook, Instagram, Twitter and WeiChat. The method of obtaining this data involves the collation of large numbers of discrete geotagged updates or photographs. Data can be collated via an application programming interface (API) provided by the app developer to researchers, by automated data scraping via computer programs, perhaps written in Python, or manually by researchers. The second type of data is continuous location-based data from applications that are designed to track movement constantly, such as Strava or MyFitnessPal. Tracking methods using this continuous location-based data are discussed in detail in the following chapter.

Social Media as Mirror of Society

2017 ◽  
pp. 128-141
Author(s):  
Amir Manzoor
Keyword(s):  
Social Media ◽  
Application Programming Interface ◽  
Social Media Data ◽  
Social Media Platform ◽  
Application Programming ◽  
Media Platform ◽  
Use Of Social Media ◽  
Media Data ◽  
Programming Interface ◽  
Specific Reason

Over the last decade, social media use has gained much attention of scholarly researchers. One specific reason of this interest is the use of social media for communication; a trend that is gaining tremendous popularity. Every social media platform has developed its own set of application programming interface (API). Through these APIs, the data available on a particular social media platform can be accessed. However, the data available is limited and it is difficult to ascertain the possible conclusions that can be drawn about society on the basis of this data. This chapter explores the ways social researchers and scientists can use social media data to support their research and analysis.

scholarly journals A Pilot Study of Valley Fever Tweets

2020 ◽  
Vol 41 (S1) ◽  
pp. s101-s101
Author(s):  
Nana Li ◽  
Gondy Leroy ◽  
Fariba Donovan ◽  
John Galgiani ◽  
Katherine Ellingson
Keyword(s):  
New York ◽  
Application Programming Interface ◽  
Washington Dc ◽  
Valley Fever ◽  
Social Media Data ◽  
The Public ◽  
Application Programming ◽  
Twitter Analysis ◽  
Media Data ◽  
Programming Interface

Background: Twitter is used by officials to distribute public health messages and by the public to post information about ongoing afflictions. Because tweets originate from geographically and socially diverse sources, scholars have used this social media data to analyze the spread of diseases like flu [Alessio Signorini 2011], asthma [Philip Harber 2019] and mental health disorders [Chandler McClellan, 2017]. To our knowledge, no Twitter analysis has been performed for Valley fever. Valley fever is a fungal infection caused by the Coccidioides organism, mostly found in Arizona and California. Objective: We analyzed tweets concerning Valley fever to evaluate content, location, and timing. Methods: We collected tweets using the Twitter search application programming interface using the terms “Valley fever,” “valleyfever,” “cocci” or “‘Valleyfever” from August 6 to 16, 2019, and again from October 20 to 29, 2019. In total, 2,117 Tweets were retrieved. Tweets not focused on Valley fever were filtered out, including a tweet about “Rift valley fever” and tweets where “valley” and “fever” were separate and not one phrase. We excluded tweets not written in English. In total, 1,533 tweets remained; we grouped them into 3 categories: original tweets, hereafter labeled “normal” (N = 497), retweets (N = 811), and replies (N = 225). We converted all terms to lowercase, removed white space and punctuation, and tokenized the tweets. Informal messaging conventions (eg, hashtag, @user, RT, links) and stop words were removed, and terms were lemmatized. Finally, we analyzed the frequency of tweets by season, state, and co-occurring terms. Results: Tweet frequency was 228.5 per week in summer and 113.4 per week in the fall. Users tweeted from 40 different states; the most common were California (N = 401; 10.1 per 100,00 population) and Arizona (N = 216, 30.1 per 100,000 population), New York (N = 49), Florida (N = 21), and Washington, DC (N = 14). Term frequency analysis showed that for normal tweets, the 5 most frequent terms were “awareness,” “Arizona,” “disease,” “California,” and “people.” For retweets, the most common terms were “Gunner” (a dog name), “vet,” “prayer,” “cough,” and “family.” For replies, they were “dog,” “lung,” “vet,” “day,” and “result.” Several symptoms were mentioned: “cough” (normal: 8, retweets: 104, and replies: 7), “sick” (normal: 21, retweets: 42, replies: 7), “rash” (normal: 2, retweets: 6, replies: 1), and “headache” (normal: 1, retweets: 3, replies: 0). Conclusions: Valley fever tweets are potentially sufficient to track disease intensity, especially in Arizona and California. Data collection over longer intervals is needed to understand the utility of Twitter in this context.Disclosures: NoneFunding: None

The Role of Smart Technologies to Support Citizen Engagement and Decision Making

2018 ◽  
Vol 14 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Gabriela Viale Pereira ◽  
Gregor Eibl ◽  
Constantinos Stylianou ◽  
Gilberto Martínez ◽  
Haris Neophytou ◽  
...  
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Real Life ◽  
Digital Technologies ◽  
Fuzzy Cognitive Maps ◽  
Data Driven ◽  
Social Media Data ◽  
Life Problems ◽  
Smart Technologies ◽  
Media Data

Smart government relies both on the application of digital technologies to enable citizen's participation in order to achieve a high level of citizen centricity and on data-driven decision making in order to improve the quality of life of citizens. Data-driven decisions in turn depend on accessible and reliable datasets, which open government and social media data are likely to promise. The SmartGov project uses digital technologies by integrating open and social media data in Fuzzy Cognitive Maps to model real life problems and simulate different scenarios leading to better decision making. This research performed a multiple-case analysis in two pilot cities. Both municipalities use the technologies to find the best routes: Limassol to improve the garbage collection and Quart de Poblet to improve the walking routes of chaperones guiding children to school. The article proposes a generic framework for Smart City Governance focusing on the inputs and outcomes of this process in the use of technologies for policy making built based on the analysis of the SmartGov.

The Role of Smart Technologies to Support Citizen Engagement and Decision Making

2022 ◽  
pp. 687-703
Author(s):  
Gabriela Viale Pereira ◽  
Gregor Eibl ◽  
Constantinos Stylianou ◽  
Gilberto Martínez ◽  
Haris Neophytou ◽  
...  
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Real Life ◽  
Digital Technologies ◽  
Data Driven ◽  
Social Media Data ◽  
Life Problems ◽  
Smart Technologies ◽  
High Level ◽  
Media Data

Smart government relies both on the application of digital technologies to enable citizen's participation in order to achieve a high level of citizen centricity and on data-driven decision making in order to improve the quality of life of citizens. Data-driven decisions in turn depend on accessible and reliable datasets, which open government and social media data are likely to promise. The SmartGov project uses digital technologies by integrating open and social media data in Fuzzy Cognitive Maps to model real life problems and simulate different scenarios leading to better decision making. This research performed a multiple-case analysis in two pilot cities. Both municipalities use the technologies to find the best routes: Limassol to improve the garbage collection and Quart de Poblet to improve the walking routes of chaperones guiding children to school. The article proposes a generic framework for Smart City Governance focusing on the inputs and outcomes of this process in the use of technologies for policy making built based on the analysis of the SmartGov.

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 Are Social Media Data Pushing Overtourism? The Case of Barcelona and Chinese Tourists

2019 ◽  
Vol 11 (12) ◽  
pp. 3356 ◽  
Author(s):  
Alonso-Almeida ◽  
Borrajo-Millán ◽  
Yi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Local Population ◽  
Travel Behaviour ◽  
Social Media Data ◽  
Large Numbers ◽  
Tourism Sector ◽  
Chinese Tourists ◽  
Media Data

Overtourism spoils the good economic and social results produced by the tourism sector, causing reductions in the quality of service of the tourist destination and rejection by the local population. Previous literature has suggested that social networks and new electronic channels could be accelerators of the process of overcrowding destinations; however, this link has not been established. For this reason, in this exploratory study, the influence of social networks on overtourism is analysed using Barcelona as a base, as Barcelona is a massively popular destination in the country that is second in the world in reception of tourists to Spain. This study is also focused on Chinese tourism, which brings large numbers of tourists and presents great economic potential. Two types of study have been used: big data techniques applied to social media with sentimental analysis, and analysis of travel packages offered in China to travel to Spain. Relevant results are obtained to understand the influence of social networks on the travel behaviour of tourists, possible contributions to overtourism, and recommendations for the management of tourism.

scholarly journals Topic Modeling Analysis of Social Enterprises: Twitter Evidence

2020 ◽  
Vol 12 (8) ◽  
pp. 3419 ◽  
Author(s):  
Shr-Wei Kao ◽  
Pin Luarn
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Application Programming Interface ◽  
Social Enterprises ◽  
Four Dimensions ◽  
Modeling Analysis ◽  
Application Programming ◽  
Rich Information ◽  
Programming Interface

Social media is a major channel used for communication by professional and social groups. The text posted on social media contains extremely rich information. To capture the development of social enterprises (SEs), this paper examines the tweets posted on Twitter and searches the hashtags on the Twitter Application Programming Interface (API) that SEs deem to be the most important. The results suggest that these tweets can be divided into three content groups (strategy, impact and business). This paper expands this into four dimensions (strategy, impact, business and people) and six indicators (social, opportunity, change, enterprise, network and team) and establishes a conceptual framework of SEs. This paper aims to enhance the understanding of the pertinent issues recently affecting SEs and extract findings that can act as a reference for follow-up studies.

scholarly journals Reddit discussions about buprenorphine associated precipitated withdrawal in the era of fentanyl

2021 ◽  
Author(s):  
Anthony Spadaro ◽  
Abeed Sarker ◽  
Whitney Hogg-Bremmer ◽  
Jennifer S Love ◽  
Nicole O'Donnell ◽  
...  
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Application Programming Interface ◽  
The United States ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Induction Strategy ◽  
Application Programming ◽  
The Impact ◽  
Media Data

Background: Buprenorphine is an evidence-based treatment for Opioid Use Disorder (OUD). Standard buprenorphine induction requires a period of opioid abstinence to minimize risk of precipitated opioid withdrawal (POW). Our objective was to study the impact of the increasing presence of fentanyl and its analogs in the opioid supply of the United States, on buprenorphine induction and POW, using social media data from Reddit. Methods: This is a data-driven, mixed methods study of opioid-related forums, called subreddits, on Reddit to analyze posts related to fentanyl, POW, and buprenorphine induction. The posts were collected from seven subreddits using an application programming interface for Reddit. We applied natural language processing to identify subsets of salient posts relevant to buprenorphine induction, and performed manual, qualitative, thematic analyses of them. Results: 267,136 posts were retrieved from seven subreddits. Fentanyl mentions increased from 3 in 2013 to 3870 in 2020, and POW mentions increased from 2 (2012) to 332 (2020). Manual review of 384 POW-mentioning posts and 106 'Bernese method' (a microdosing induction strategy) mentioning posts revealed common themes and peoples' experiences. Specifically, presence of fentanyl caused POWs despite long abstinence durations, and alternative induction via microdosing were frequently recommended in peer-to-peer discussions. Conclusions: This study found that increased social media chatter on Reddit about POW correlated with fentanyl mentions. A subset of posts described microdosing as a self-management strategy to avoid POW. Reddit posts suggest that people are utilizing these strategies to initiate buprenorphine due to challenges arising from fentanyl prevalence in the opioid supply.

scholarly journals Implementasi Metode Klasifikasi Naive Bayes Pada Sistem Analisis Opini Pengguna Twitter Berbasis Web

2021 ◽  
Vol 10 (1) ◽  
pp. 46-54
Author(s):  
Apif Supriadi ◽  
Fatmasari
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Application Programming Interface ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Public Sentiment ◽  
Application Programming ◽  
Analysis System ◽  
Programming Interface

Abstract— Development of social media which is the result of technological development is an inseparable part of people's lives. Social media is a place where ordinary people express their feelings and opinions about something that concerns them. Inknowing the direction of public sentiment, surveys are usually done online or offline, this sentiment analysis system will facilitate and speed up the process of knowing the direction of public sentiment, in the case of research. This uses data from Twitter social media called tweets or tweets, web-based sentiment analysis system that will classify tweets into 3 (three) types of sentiments, namely positive, neutral and negative, then make a percentage to make it easier to see the direction of public sentiment. In classifying this system uses the Naive Bayes Classifier method and displays it in a web interface with the PHP programming language and uses the Application Programming Interface (API) to get data from Twitter. Intisari — Saat ini perkembangan media sosial yang merupakan hasil dari perkembangan teknologi menjadi bagian tak terpisahkan dari kehidupan masyarakat. Media sosial menjadi tempat masyarakat biasa mengutarakan berbagai perasaan dan opininya tentang suatu hal yang jadi perhatian mereka, dalam mengetahui arah sentimen masyarakat biasanya dilakukan survei baik secara online atau offline, sistem analisis sentimen ini akan memudahkan dan mempercepat proses mengetahui arah sentimen publik, dalam kasus penelitian ini menggunakan data dari media sosial Twitter yang disebut dengan tweets atau cuitan, sistem analisis sentimen berbasis web yang akan mengklasifikasikan cuitan kedalam 3 (tiga) jenis sentimen yaitu positif, netral dan negatif lalu melakukan persentasenya agar mempermudah melihat arah sentimen publik. Dalam melakukan klasifikasinya sistem ini menggunakan metode Naive Bayes Classifier dan menampilkannya dalam antarmuka web dengan bahasa pemrograman PHP dan menggunakan Application Programming Interface (API) dalam mendapatkan data dari Twitter.

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