Ontology Meter for Twitter Fake Accounts Detection

One of the most popular social media platforms, Twitter is used by millions of people to share information, broadcast tweets, and follow other users. Twitter is an open application programming interface and thus vulnerable to attack from fake accounts, which are primarily created for advertisement and marketing, defamation of an individual, consumer data acquisition, increase fake blog or website traffic, share disinformation, online fraud, and control. Fake accounts are harmful to both users and service providers, and thus recognizing and filtering out such content on social media is essential. This study presents a new approach to detect fake Twitter accounts using ontology and Semantic Web Rule Language (SWRL) rules. SWRL rules-based reasoner is utilized under predefined rules to infer whether the profile is trust or fake. This approach achieves a high detection accuracy of 97%. Furthermore, ontology classifier is an interpretable model that offers straightforward and human-interpretable decision rules.

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Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set (Preprint)

10.2196/preprints.19273 ◽

2020 ◽

Author(s):

Emily Chen ◽

Kristina Lerman ◽

Emilio Ferrara

Keyword(s):

Social Media ◽

Data Collection ◽

Application Programming Interface ◽

Time Data ◽

Data Set ◽

Twitter Data ◽

Social Media Platforms ◽

Application Programming ◽

Twitter Activity ◽

Online Conversation

BACKGROUND At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter. OBJECTIVE In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository. METHODS We started this ongoing data collection on January 28, 2020, leveraging Twitter’s streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter’s search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020. RESULTS Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events. CONCLUSIONS It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic—and undoubtedly more.

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Using Twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines

Postgraduate Medical Journal ◽

10.1136/postgradmedj-2021-140685 ◽

2021 ◽

pp. postgradmedj-2021-140685

Author(s):

Robert Marcec ◽

Robert Likic

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

English Language ◽

Vaccination Campaign ◽

Application Programming Interface ◽

Social Media Platforms ◽

Application Programming ◽

Source Of Information ◽

Programming Interface ◽

Over Time

IntroductionA worldwide vaccination campaign is underway to bring an end to the SARS-CoV-2 pandemic; however, its success relies heavily on the actual willingness of individuals to get vaccinated. Social media platforms such as Twitter may prove to be a valuable source of information on the attitudes and sentiment towards SARS-CoV-2 vaccination that can be tracked almost instantaneously.Materials and methodsThe Twitter academic Application Programming Interface was used to retrieve all English-language tweets mentioning AstraZeneca/Oxford, Pfizer/BioNTech and Moderna vaccines in 4 months from 1 December 2020 to 31 March 2021. Sentiment analysis was performed using the AFINN lexicon to calculate the daily average sentiment of tweets which was evaluated longitudinally and comparatively for each vaccine throughout the 4 months.ResultsA total of 701 891 tweets have been retrieved and included in the daily sentiment analysis. The sentiment regarding Pfizer and Moderna vaccines appeared positive and stable throughout the 4 months, with no significant differences in sentiment between the months. In contrast, the sentiment regarding the AstraZeneca/Oxford vaccine seems to be decreasing over time, with a significant decrease when comparing December with March (p<0.0000000001, mean difference=−0.746, 95% CI=−0.915 to −0.577).ConclusionLexicon-based Twitter sentiment analysis is a valuable and easily implemented tool to track the sentiment regarding SARS-CoV-2 vaccines. It is worrisome that the sentiment regarding the AstraZeneca/Oxford vaccine appears to be turning negative over time, as this may boost hesitancy rates towards this specific SARS-CoV-2 vaccine.

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#ColonCancer: Social Media Discussions About Colorectal Cancer During the COVID-19 Pandemic

JCO Clinical Cancer Informatics ◽

10.1200/cci.21.00180 ◽

2022 ◽

Author(s):

Vishal R. Patel ◽

Sofia Gereta ◽

Christopher J. Blanton ◽

Alexander L. Chu ◽

Neha K. Reddy ◽

...

Keyword(s):

Colorectal Cancer ◽

Social Media ◽

Early Detection ◽

Language Processing ◽

Application Programming Interface ◽

Cancer Awareness ◽

Personal Experiences ◽

Social Media Platforms ◽

Application Programming

PURPOSE Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. Social media platforms such as Twitter are extensively used to communicate about cancer care, yet little is known about the role of these online platforms in promoting early detection or sharing the lived experiences of patients with CRC. This study tracked Twitter discussions about CRC and characterized participating users to better understand public communication and perceptions of CRC during the COVID-19 pandemic. METHODS Tweets containing references to CRC were collected from January 2020 to April 2021 using Twitter's Application Programming Interface. Account metadata was used to predict user demographic information and classify users as either organizations, individuals, clinicians, or influencers. We compared the number of impressions across users and analyzed the content of tweets using natural language processing models to identify prominent topics of discussion. RESULTS There were 72,229 unique CRC-related tweets by 31,170 users. Most users were male (66%) and older than 40 years (57%). Individuals accounted for most users (44%); organizations (35%); clinicians (19%); and influencers (2%). Influencers made the most median impressions (35,853). Organizations made the most overall impressions (1,067,189,613). Tweets contained the following topics: bereavement (20%), appeals for early detection (20%), research (17%), National Colorectal Cancer Awareness Month (15%), screening access (14%), and risk factors (14%). CONCLUSION Discussions about CRC largely focused on bereavement and early detection. Online coverage of National Colorectal Cancer Awareness Month and personal experiences with CRC effectively stimulated goal-oriented tweets about early detection. Our findings suggest that although Twitter is commonly used for communicating about CRC, partnering with influencers may be an effective strategy for improving communication of future public health recommendations related to CRC.

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Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set

JMIR Public Health and Surveillance ◽

10.2196/19273 ◽

2020 ◽

Vol 6 (2) ◽

pp. e19273 ◽

Cited By ~ 36

Author(s):

Emily Chen ◽

Kristina Lerman ◽

Emilio Ferrara

Keyword(s):

Social Media ◽

Data Collection ◽

Application Programming Interface ◽

Time Data ◽

Data Set ◽

Twitter Data ◽

Social Media Platforms ◽

Application Programming ◽

Twitter Activity ◽

Online Conversation

Background At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter. Objective In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository. Methods We started this ongoing data collection on January 28, 2020, leveraging Twitter’s streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter’s search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020. Results Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events. Conclusions It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic—and undoubtedly more.

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Is JUUL marketing to kids on social media? Analysis of JUUL Twitter Followers� Age (Preprint)

10.2196/preprints.11580 ◽

2018 ◽

Author(s):

Annice Kim ◽

Robert Chew ◽

Michael Wenger ◽

Margaret Cress ◽

Thomas Bukowski ◽

...

Keyword(s):

Social Media ◽

Application Programming Interface ◽

Media Analysis ◽

Prediction Algorithm ◽

Social Media Networks ◽

Verification Methods ◽

Twitter Account ◽

Application Programming ◽

Age Verification ◽

Age Category

BACKGROUND JUUL is an electronic nicotine delivery system (ENDS) resembling a USB device that has become rapidly popular among youth. Recent studies suggest that social media may be contributing to its popularity. JUUL company claims their products are targeted for adult current smokers but recent surveillance suggests youth may be exposed to JUUL products online. To date, there has been little attention on restricting youth exposure to age restricted products on social media. OBJECTIVE The objective of this study was to utilize a computational age prediction algorithm to determine the extent to which underage youth are being exposed to JUUL’s marketing practices on Twitter. METHODS We examined all of @JUULvapor’s Twitter followers in April 2018. For followers with a public account, we obtained their metadata and last 200 tweets using the Twitter application programming interface. We ran a series of classification models to predict whether the account following @JUULvapor was an underage youth or an adult. RESULTS Out of 9,077 individuals following @JUULvapor Twitter account, a three-age category model predicted that 44.9% are 13 to 17 years old (N=4,078), 43.6% are 18 to 24 years old (N=3,957), and 11.5% are 25 years old or older (N=1,042); and a two-age category model predicted that 80.6% (N=7,313) are under 21 years old. CONCLUSIONS Despite a disclaimer that followers must be of legal age to purchase tobacco products, the majority of JUUL followers on Twitter are under age. This suggests that ENDS brands and social media networks need to implement more stringent age-verification methods to protect youth from age-restricted content.

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Cooperative Business Intelligence Model Using a Multiagent Platform

Scientific Programming ◽

10.1155/2020/8898719 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Mohamed O. Khozium ◽

Norah S. Farooqi

Keyword(s):

Business Intelligence ◽

Service Providers ◽

Application Programming Interface ◽

Cooperative Interaction ◽

End User ◽

Agent Based ◽

Distributed Services ◽

Intelligence System ◽

Application Programming ◽

New Applications

Modern companies wish to utilize business intelligence (BI) to track and analyze their courses of action. Many BI applications serve this purpose at many levels, starting from documenting and charting and ending with analytics and decision support systems, which are considered a sufficient complement to consultancy and management resources. However, the contemporaneous BI software is missing two functionalities. First, although nearly all applications of the same genre use almost identical concepts, there is no unified application programming interface (API) to enable interaction. The second problem is a consequence of the first issue. Without a unified API, BI applications cannot be integrated, eliminating any possibility of establishing universal platforms for BI distributed services. Lacking these two functionalities makes developers reinvent the wheel with each new implementation. To solve these problems, we propose a platform running a multiagent business intelligence system. This system empowers the available BI resources to serve a larger segment of the BI end-user applications cooperatively. To build this system, we propose a unified model that enables distributive agent-based tasking and cooperative interaction. This allows researchers to cooperate in spreading the multiagent platform’s functionality and helps them proceed toward more detailed analysis considering agents’ construction. Moreover, it will enable BI service providers to cooperatively implement new applications and develop better solutions while maintaining a functional end-user program.

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How changing API terms changed Instagram's domain? A bibliometric analysis

10.31219/osf.io/c4m7s ◽

2021 ◽

Author(s):

Richard Hartman ◽

Tereza Simova

Keyword(s):

Social Media ◽

Well Being ◽

Body Image Dissatisfaction ◽

Research Area ◽

Before And After ◽

Social Media Platforms ◽

Application Programming ◽

Key Topics ◽

Media Research ◽

Social Media Research

In 2018 Facebook blocked a public Application Programming Interfaces (API) that could be used to download data from Facebook and Instagram. Much uncertainty still exists about the effect on social media research due to changes in Instagram API conditions. The presented paper provides an overview of the Instagram domain in terms of a research area. The main focus of this research is on the comparison of the key topics before and after the change of the Instagram API terms (comparing Instagram's research domain before and after 2018). A partial goal was to find out how the change in the conditions of the Instagram API has changed the number of social media research itself. We used a bibliometric approach to map the domain of Instagram. The paper has identified key topics in the domain of Instagram. Between the years 2010 and 2018 the key topics were gender, behavior on social media, dissemination of information, and platform selection. After the change of Instagram API conditions, after 2018, the key topics were gratifications, body image, dissatisfaction, and basic Instagram topics. The paper has found that generally, there was no change in research topics, nor the number of papers published after the Instagram API condition. Further study should focus on establish the relationships between Instagram use and psychological well-being; investigate the motives for Instagram use a study the effect of Instagram API on research with the use of different methods; gaining a better understanding of social media consumer activity; establish whatever our key topics are relevant to other social media platforms (Facebook, Twitter or Tiktok); study Instagram domain on different citation databases (e.g., in Scopus). This paper has also raised important questions about whether the Instagram API should be or should not be open for research purposes.

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Social media and the spread of COVID-19 conspiracy theories in Nigeria

Journal of Ideas in Health ◽

10.47108/jidhealth.vol4.iss3.150 ◽

2021 ◽

Vol 4 (3) ◽

pp. 432-437

Author(s):

Sarah Gambo ◽

Woyopwa Shem

Keyword(s):

Social Media ◽

Subject Matter ◽

Research Interest ◽

Conspiracy Theories ◽

The Social ◽

Recent Outbreak ◽

Social Media Platforms ◽

The Subject ◽

National Orientation ◽

And Control

Background: Amidst the recent outbreak of the Covid-19 pandemic, there seems to be an avalanche of conspiracy theories that abound on social media platforms, and this subject attracted a lot of research interest. This study aimed to examine the "social media and the spread Covid-19 conspiracy theories in Nigeria" in light of the above. Methods: The study adopted a qualitative design in order to explore the subject matter thoroughly. Thirty-five participants were conveniently sampled, and interviews were conducted to retrieved data from the participants. Results: Findings of this study revealed that there is a prevalence of conspiracy theories that have saturated social media ever since the outbreak of the Covid-19 pandemic. It was also found that ignorance, religious fanaticism, lack of censorship, and insufficient counter information on social media platforms are some of the possible factors that aided the spread of Covid-19 conspiracy theories among Nigerian social media users. Conclusion: This study recommends, among other things, that there is a swift need to curtail the spread of conspiracy theories through consistent dissemination of counter-information by both individuals and agencies like the National Orientation Agency (NOA) and the Nigerian Centre for Disease and Control (NCDC).

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The public accountability of social platforms: lessons from a study on bots and trolls in the Brexit campaign

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2018.0003 ◽

2018 ◽

Vol 376 (2128) ◽

pp. 20180003 ◽

Cited By ~ 8

Author(s):

Marco Bastos ◽

Dan Mercea

Keyword(s):

Social Media ◽

Large Scale ◽

Ethical Issues ◽

Lessons Learned ◽

Information Warfare ◽

The Public ◽

Social Media Platforms ◽

Application Programming ◽

Access To Data ◽

The Uk

In this article, we review our study of 13 493 bot-like Twitter accounts that tweeted during the UK European Union membership referendum debate and disappeared from the platform after the ballot. We discuss the methodological challenges and lessons learned from a study that emerged in a period of increasing weaponization of social media and mounting concerns about information warfare. We address the challenges and shortcomings involved in bot detection, the extent to which disinformation campaigns on social media are effective, valid metrics for user exposure, activation and engagement in the context of disinformation campaigns, unsupervised and supervised posting protocols, along with infrastructure and ethical issues associated with social sciences research based on large-scale social media data. We argue for improving researchers' access to data associated with contentious issues and suggest that social media platforms should offer public application programming interfaces to allow researchers access to content generated on their networks. We conclude with reflections on the relevance of this research agenda to public policy. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations'.

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Rwandan diaspora online: Social connections and identity narratives

Crossings Journal of Migration and Culture ◽

10.1386/cjmc_00004_1 ◽

2019 ◽

Vol 10 (2) ◽

pp. 223-241

Author(s):

Michelle Martin

Keyword(s):

Social Media ◽

Thematic Analysis ◽

Collective Identity ◽

Service Providers ◽

Migration Policy ◽

Social Connections ◽

Virtual Ethnography ◽

Identity Narratives ◽

Social Media Platforms ◽

Cultural Connections

This article explores how Rwandan diaspora living in North America and Europe use social media platforms to establish networked connections and express a range of identity narratives related to their forced displacement and resettlement experiences. Facebook posts (and cross-posted tweets), including status updates and linked artefacts, posted by members of the Rwandan diaspora were analysed using thematic analysis, borrowing concepts from virtual ethnography. Results reveal that Rwandan diaspora active on social media used Facebook and Twitter extensively to connect with homeland compatriots and to express a range of identity narratives with strong historic and cultural connections. Trauma related to their displacement and resettlement experiences was prevalent throughout the data and was strongly integrated into diaspora members’ collective identity. Contributions to migration policy and service providers working with trauma-exposed migrants are explored.

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