On February 2, 2020, the World Health Organization declared a COVID-19 social media "infodemic", with special attention to misinformation -- frequently understood as false claims. To understand the infodemic's scope and scale, we analyzed over 500 million posts from Twitter and Facebook about COVID-19 and other health topics, between March 8 and May 1, 2020. Following prior work, we assumed URL source credibility is a proxy for false content, but we also tested this assumption. Contrary to expectations, we found that messages about COVID-19 were more likely to contain links to more credible sources. Additionally, messages linking to government sources, and to news with intermediate credibility, were shared more often, on average, than links to non-credible sources. These results suggest that more ambiguous forms of misinformation about COVID-19 may be more likely to be disseminated through credible sources when compared to other health topics. Furthermore, the assumption that credibility is an adequate proxy for false content may overestimate the prevalence of false content online: less than 25% of posts linking to the least credible sources contained false content. Our results emphasize the importance of distinguishing between explicit falsehoods and more ambiguous forms of misinformation due to the search for meaning in an environment of scientific uncertainty.
Multiple studies in the past have analyzed the role and dynamics of the Twitter social network during real world events. However, little work has explored the content of other social media services, or compared content across two networks during real world events. We believe that social media platforms like Facebook also play a vital role in disseminating information on the Internet during real world events. In this work, we study and characterize the content posted on the world's biggest social network, Facebook, and present a comparative analysis of Facebook and Twitter content posted during…
In this analysis, we work with the data set that was compiled by Darren Linvill and Patrick Warren, along with a representative sample of Facebook ads that were released by the House Intelligence Committee Minority. The goal of this analysis is to use the categories defined by Linvill and Warren in the Twitter data and investigate if these categories exist in Facebook ads. This begin to give us insights to the tactics used between the two social media services. Further, we try to replicate Linvill and Warren's original categorization of the Twitter data. Lastly, we investigate what categories …
During sudden onset crisis events, the presence of spam, rumors and fake content on Twitter reduces the value of information contained on its messages (or "tweets"). A possible solution to this problem is to use machine learning to automatically evaluate the credibility of a tweet, i.e. whether a person would deem the tweet believable or trustworthy. This has been often framed and studied as a supervised classification problem in an off-line (post-hoc) setting. In this paper, we present a semi-supervised ranking model for scoring tweets according to their credibility. This model is used in Twe…
We investigate the link sharing behavior of Twitter users following the temporary halt of AstraZeneca COVID-19 vaccine development in September 2020. During this period, we show the presence of malicious and low credibility information sources shared on Twitter messages in multiple languages. The malicious URLs, often in shortened forms, are increasingly hosted in content delivery networks and shared cloud hosting infrastructures not only to improve reach but also to avoid being detected and blocked. There are potential signs of coordination to promote both malicious and low credibility URLs o…
In this paper we compare the social network structure of people talking about Crohn's disease, Cystic Fibrosis, and Type 1 diabetes on Facebook and Twitter. We find that the Crohn's community's contributors are most emotional on Facebook and Twitter and most negative on Twitter, while the T1D community's communication network structure is most cohesive.
We measure the impact of "altmetrics" field by deploying altmetrics indicators using the data from Google Scholar, Twitter, Mendeley, Facebook, Google-plus, CiteULike, Blogs and Wiki during 2010- 2014. To capture the social impact of scientific publications, we propose an index called alt-index, analogues to h-index. Across the deployed indices, our results have shown high correlation among the indicators that capture social impact. While we observe medium Pearson's correlation (\r{ho}= .247) among the alt-index and h-index, a relatively high correlation is observed between social citations an…
As more and more people use social media to communicate their view and perception of elections, researchers have increasingly been collecting and analyzing data from social media platforms. Our research focuses on social media communication related to the 2013 election of the German parlia-ment [translation: Bundestagswahl 2013]. We constructed several social media datasets using data from Facebook and Twitter. First, we identified the most relevant candidates (n=2,346) and checked whether they maintained social media accounts. The Facebook data was collected in November 2013 for the period of…
The present study argues that political communication on social media is mediated by a platform's digital architecture, defined as the technical protocols that enable, constrain, and shape user behavior in a virtual space. A framework for understanding digital architectures is introduced, and four platforms (Facebook, Twitter, Instagram, and Snapchat) are compared along the typology. Using the 2016 US election as a case, interviews with three Republican digital strategists are combined with social media data to qualify the studyies theoretical claim that a platform's network structure, functio…
We present VaccinItaly, a project which monitors Italian online conversations around vaccines, on Twitter and Facebook. We describe the ongoing data collection, which follows the SARS-CoV-2 vaccination campaign roll-out in Italy and we provide public access to the data collected. We show results from a preliminary analysis of the spread of low- and high-credibility news shared alongside vaccine-related conversations on both social media platforms. We also investigate the content of most popular YouTube videos and encounter several cases of harmful and misleading content about vaccines. Finally…
This workshop paper reports on an ongoing mixed-methods study on the two arguably most popular social network sites, Facebook and Twitter, for the same users. The overarching goal of the study is to shed light into the nuances of social media selection and cross-platform use by combining survey data about participants' motivations with usage data collected via API extraction. We describe the set-up of the study and focus our discussion on the challenges and insights relating to participant recruiting and data collection, handling and dimensionalizing usage data, and comparing usage data across…
Background: Tools used to appraise the credibility of health information are time-consuming to apply and require context-specific expertise, limiting their use for quickly identifying and mitigating the spread of misinformation as it emerges. Our aim was to estimate the proportion of vaccination-related posts on Twitter are likely to be misinformation, and how unevenly exposure to misinformation was distributed among Twitter users. Methods: Sampling from 144,878 vaccination-related web pages shared on Twitter between January 2017 and March 2018, we used a seven-point checklist adapted from two…
Vaccinations play a critical role in mitigating the impact of COVID-19 and other diseases. This study explores COVID-19 vaccine misinformation circulating on Twitter during 2021, when vaccines were being released to the public in an effort to mitigate the global pandemic. Our findings show a low prevalence of low-credibility information compared to mainstream news. However, most popular low-credibility sources had larger reshare volumes than authoritative sources such as the CDC and WHO. We observed an increasing trend in the prevalence of low-credibility news relative to mainstream news about…
Professional players in social media (e.g., big companies, politician, athletes, celebrities, etc) are intensively using Online Social Networks (OSNs) in order to interact with a huge amount of regular OSN users with different purposes (marketing campaigns, customer feedback, public reputation improvement, etc). Hence, due to the large catalog of existing OSNs, professional players usually count with OSN accounts in different systems. In this context an interesting question is whether professional users publish the same information across their OSN accounts, or actually they use different OSNs…
We measure trends in the diffusion of misinformation on Facebook and Twitter between January 2015 and July 2018. We focus on stories from 570 sites that have been identified as producers of false stories. Interactions with these sites on both Facebook and Twitter rose steadily through the end of 2016. Interactions then fell sharply on Facebook while they continued to rise on Twitter, with the ratio of Facebook engagements to Twitter shares falling by approximately 60 percent. We see no similar pattern for other news, business, or culture sites, where interactions have been relatively stable ov…
In recent past online social media has played a pivotal role in sharing of information and opinions on real time events. Events in physical space are reflected in digital world through online social networks. Event based studies for such content have been widely done on Twitter in the computer science community. In this report, we performed a detailed analysis of a sports event called the Indian Premier League (IPL'13) for both Facebook and Twitter. IPL is the most popular cricket league in India with players from across the world. We analysed more than 2.6 million tweets and 700 thousand Face…
Micro-blogs are contemporary broadcasting services for exchanging small elements of content, including video and images. Despite its popularity, micro-blogging is not without issues. So far, various security concerns, such as: privacy and confidentiality of micro-blogging systems have attracted the interest of the scientific community. Nevertheless, in this document we refer to a security issue that is concerned with the posting and circulation of fake messages. With the aim to make micro-blogs a credible source of information, in this work we propose a protocol, as a solution, that can be eas…
The global spread of the novel coronavirus is affected by the spread of related misinformation -- the so-called COVID-19 Infodemic -- that makes populations more vulnerable to the disease through resistance to mitigation efforts. Here we analyze the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media platforms, Twitter and Facebook. We characterize cross-platform similarities and differences in popular sources, diffusion patterns, influencers, coordination, and automation. Comparing the two platforms, we find divergence among the preval…
Social networking and micro-blogging services, such as Twitter, play an important role in sharing digital information. Despite the popularity and usefulness of social media, there have been many instances where corrupted users found ways to abuse it, as for instance, through raising or lowering user's credibility. As a result, while social media facilitates an unprecedented ease of access to information, it also introduces a new challenge - that of ascertaining the credibility of shared information. Currently, there is no automated way of determining which news or users are credible and which …
Fake news are affecting a large proportion of the population even becoming a danger to the society. Mostly, this disinformation flow take place through Internet. Being aware of that problem, in this work we propose a synthetic indicator that measures the user reputation in Twitter in order to analyze the credibility of the content in this social network. In order to show the indicator utility, we have analyzed data from some political topics in Spain from 2019 to 2020 and we have checked that bots plays a decisive role into the spread of news and, as might be expected, the link among popularit…
Religiosity is a powerful force shaping human societies, affecting domains as diverse as economic growth or the ability to cope with illness. As more religious leaders and organizations as well as believers start using social networking sites (e.g., Twitter, Facebook), online activities become important extensions to traditional religious rituals and practices. However, there has been lack of research on religiosity in online social networks. This paper takes a step toward the understanding of several important aspects of religiosity on Twitter, based on the analysis of more than 250k U.S. use…
With a substantial proportion of the population currently hesitant to take the COVID-19 vaccine, it is important that people have access to accurate information. However, there is a large amount of low-credibility information about vaccines spreading on social media. In this paper, we present the CoVaxxy dataset, a growing collection of English-language Twitter posts about COVID-19 vaccines. Using one week of data, we provide statistics regarding the numbers of tweets over time, the hashtags used, and the websites shared. We also illustrate how these data might be utilized by performing an ana…
The Russia-based Internet Research Agency (IRA) carried out a broad information campaign in the U.S. before and after the 2016 presidential election. The organization created an expansive set of internet properties: web domains, Facebook pages, and Twitter bots, which received traffic via purchased Facebook ads, tweets, and search engines indexing their domains. We investigate the scope of IRA activities in 2017, joining data from Facebook and Twitter with logs from the Internet Explorer 11 and Edge browsers and the Bing.com search engine. The studies demonstrate both the ease with which malic…
Opposition and hesitancy to vaccination have been one of the major threats to global health. Social media sites have been suspected as a breeding ground of misleading narratives about vaccines, but little is known about how pervasive anti-vaccine views are on the world's largest social media, Facebook. Here, we study the prevalence of online groups on Facebook with anti- and pro-vaccine views and the information ecosystem that enables the production and dissemination of anti-vaccine narratives, using the largest collection of Facebook data on this issue to date. The results reveal that 74% of …
While Twitter provides an unprecedented opportunity to learn about breaking news and current events as they happen, it often produces skepticism among users as not all the information is accurate but also hoaxes are sometimes spread. While avoiding the diffusion of hoaxes is a major concern during fast-paced events such as natural disasters, the study of how users trust and verify information from tweets in these contexts has received little attention so far. We survey users on credibility perceptions regarding witness pictures posted on Twitter related to Hurricane Sandy. By examining credibi…
ITMO University（圣光机大学）；SUSS School of Business（新跃社科大学商学院）；SoMin.ai Research（SoMin.ai研究院）
Human personality traits are the key drivers behind our decision-making, influencing our life path on a daily basis. Inference of personality traits, such as Myers-Briggs Personality Type, as well as an understanding of dependencies between personality traits and users' behavior on various social media platforms is of crucial importance to modern research and industry applications. The emergence of diverse and cross-purpose social media avenues makes it possible to perform user personality profiling automatically and efficiently based on data represented across multiple data modalities. Howeve…
Many in the US were reluctant to report their COVID-19 cases at the height of the pandemic (e.g., for fear of missing work or other obligations due to quarantine mandates). Other methods such as using public social media data can therefore help augment current approaches to surveilling pandemics. This study evaluated the effectiveness of using social media data as a data source for tracking public health pandemics. There have been several attempts at using social media data from platforms like Twitter for analyzing the COVID-19 pandemic. While these provide a multitude of useful insights, new …
Users of social media sites like Facebook and Twitter rely on crowdsourced content recommendation systems (e.g., Trending Topics) to retrieve important and useful information. Contents selected for recommendation indirectly give the initial users who promoted (by liking or posting) the content an opportunity to propagate their messages to a wider audience. Hence, it is important to understand the demographics of people who make a content worthy of recommendation, and explore whether they are representative of the media site's overall population. In this work, using extensive data collected fro…
During the summer of 2018, Facebook, Google, and Twitter created policies and implemented transparent archives that include U.S. political advertisements which ran on their platforms. Through our analysis of over 1.3 million ads with political content, we show how different types of political advertisers are disseminating U.S. political messages using Facebook, Google, and Twitter's advertising platforms. We find that in total, ads with political content included in these archives have generated between 8.67 billion - 33.8 billion impressions and that sponsors have spent over $300 million USD …
Social media have become part of modern news reporting, used by journalists to spread information and find sources, or as a news source by individuals. The quest for prominence and recognition on social media sites like Twitter can sometimes eclipse accuracy and lead to the spread of false information. As a way to study and react to this trend, we introduce {\sc TwitterTrails}, an interactive, web-based tool ({\tt twittertrails.com}) that allows users to investigate the origin and propagation characteristics of a rumor and its refutation, if any, on Twitter. Visualizations of burst activity, p…
Facebook and Twitter recently announced community-based review platforms to address misinformation. We provide an overview of the potential affordances of such community-based approaches to content moderation based on past research and preliminary analysis of Twitter's Birdwatch data. While our analysis generally supports a community-based approach to content moderation, it also warns against potential pitfalls, particularly when the implementation of the new infrastructure focuses on crowd-based "validation" rather than "collaboration." We call for multidisciplinary research utilizing methods…
First identified in Wuhan, China, in December 2019, the outbreak of COVID-19 has been declared as a global emergency in January, and a pandemic in March 2020 by the World Health Organization (WHO). Along with this pandemic, we are also experiencing an "infodemic" of information with low credibility such as fake news and conspiracies. In this work, we present ReCOVery, a repository designed and constructed to facilitate research on combating such information regarding COVID-19. We first broadly search and investigate ~2,000 news publishers, from which 60 are identified with extreme [high or low…
University of Pennsylvania（宾夕法尼亚大学）；Emory University（埃默里大学）
With the increasing use of social media data for health-related research, the credibility of the information from this source has been questioned as the posts may originate from automated accounts or "bots". While automatic bot detection approaches have been proposed, there are none that have been evaluated on users posting health-related information. In this paper, we extend an existing bot detection system and customize it for health-related research. Using a dataset of Twitter users, we first show that the system, which was designed for political bot detection, underperforms when applied to…
Nowadays social media (Twitter, Facebook, etc.), not only simply as communication media, but also for promotion. Social networking media offers many business benefits for companies and organizations. Research purposes is to determine the model of social network media utilization as a promotional media for handicraft business in Palembang city. Qualitative and quantitative research design are used to know how handicraft business in Palembang city utilizing social media networking as a promotional media. The research results show 35% craft businesses already utilizing social media as a promotion…
What kinds of social media users read junk news? We examine the distribution of the most significant sources of junk news in the three months before President Donald Trump first State of the Union Address. Drawing on a list of sources that consistently publish political news and information that is extremist, sensationalist, conspiratorial, masked commentary, fake news and other forms of junk news, we find that the distribution of such content is unevenly spread across the ideological spectrum. We demonstrate that (1) on Twitter, a network of Trump supporters shares the widest range of known j…
What do college students reveal to their peers on social media under complete anonymity? Do their campus environments relate to the topics of their disclosure? To answer these questions, I analyze Facebook confessions pages. Popular on hundreds of college campuses, these pages allow students to anonymously post personal confessions on a public community forum. In this preliminary research note, I analyze several explanatory factors of online student confessional behavior. Aggregating nearly 200,000 confessions posts spanning a period of 3 years, I combine Latent Dirichlet Allocation (LDA) with…
University of Ottawa（渥太华大学）；Institute for Big Data Analytics, Dalhousie University（达尔豪斯大学大数据分析研究所）；Institute of Computer Science, Polish Academy of Sciences（波兰科学院计算机科学研究所）；Peacegeeks（和平极客）；iHUB
The tremendous growth of social media content on the Internet has inspired the development of the text analytics to understand and solve real-life problems. Leveraging statistical topic modelling helps researchers and practitioners in better comprehension of textual content as well as provides useful information for further analysis. Statistical topic modelling becomes especially important when we work with large volumes of dynamic text, e.g., Facebook or Twitter datasets. In this study, we summarize the message content of four data sets of Twitter messages relating to challenging social event…
The rising popularity of social media has radically changed the way news content is propagated, including interactive attempts with new dimensions. To date, traditional news media such as newspapers, television and radio have already adapted their activities to the online news media by utilizing social media, blogs, websites etc. This paper provides some insight into the social media presence of worldwide popular news media outlets. Despite the fact that these large news media propagate content via social media environments to a large extent and very little is known about the news item produce…
Al Imam Mohammad Ibn Saud Islamic University（伊玛目穆罕默德·本·沙特伊斯兰大学）；King Saud University（沙特国王大学）
Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested cl…
Social media platforms like Facebook and Twitter permit experiments to be performed at minimal cost on populations of a size that scientists might previously have dreamt about. For instance, one experiment on Facebook involved over 60 million subjects. Such large scale experiments introduce new challenges as even small effects when multiplied by a large population can have a significant impact. Recent revelations about the use of social media to manipulate voting behaviour compound such concerns. It is believed that the psychometric data used by Cambridge Analytica to target US voters was coll…
In the era when Facebook and Twitter dominate the market for social media, Google has introduced Google+ (G+) and reported a significant growth in its size while others called it a ghost town. This begs the question that "whether G+ can really attract a significant number of connected and active users despite the dominance of Facebook and Twitter?". This paper tackles the above question by presenting a detailed characterization of G+ based on large scale measurements. We identify the main components of G+ structure, characterize the key features of their users and their evolution over time. We…
Online social media are key platforms for the public to discuss political issues. As a result, researchers have used data from these platforms to analyze public opinions and forecast election results. Recent studies reveal the existence of inauthentic actors such as malicious social bots and trolls, suggesting that not every message is a genuine expression from a legitimate user. However, the prevalence of inauthentic activities in social data streams is still unclear, making it difficult to gauge biases of analyses based on such data. In this paper, we aim to close this gap using Twitter data…
The pandemic required efficient allocation of public resources and transforming existing ways of societal functions. To manage any crisis, governments and public health researchers exploit the information available to them in order to make informed decisions, also defined as situational awareness. Gathering situational awareness using social media has been functional to manage epidemics. Previous research focused on using discussions during periods of epidemic crises on social media platforms like Twitter, Reddit, or Facebook and developing NLP techniques to filter out relevant discussions fro…
Wroclaw University of Science and Technology（弗罗茨瓦夫理工大学）
Every day, the world is flooded by millions of messages and statements posted on Twitter or Facebook. Social media platforms try to protect users' personal data, but there still is a real risk of misuse, including elections manipulation. Did you know, that only 13 posts addressing important or controversial topics for society are enough to predict one's political affiliation with a 0.85 F1-score? To examine this phenomenon, we created a novel universal method of semi-automated political leaning discovery. It relies on a heuristical data annotation procedure, which was evaluated to achieve 0.95…
With the increasing use of online social networks as a source of news and information, the propensity for a rumor to disseminate widely and quickly poses a great concern, especially in disaster situations where users do not have enough time to fact-check posts before making the informed decision to react to a post that appears to be credible. In this study, we explore the propagation pattern of rumors on Twitter by exploring the dynamics of microscopic-level misinformation spread, based on the latent message and user interaction attributes. We perform supervised learning for feature selection …
False information and true information fact checking it, often co-exist in social networks, each competing to influence people in their spread paths. An efficient strategy here to contain false information is to proactively identify if nodes in the spread path are likely to endorse false information (i.e. further spread it) or refutation information (thereby help contain false information spreading). In this paper, we propose SCARLET (truSt and Credibility bAsed gRaph neuraL nEtwork model using aTtention) to predict likely action of nodes in the spread path. We aggregate trust and credibility …
State-sponsored organizations are increasingly linked to efforts aimed to exploit social media for information warfare and manipulating public opinion. Typically, their activities rely on a number of social network accounts they control, aka trolls, that post and interact with other users disguised as "regular" users. These accounts often use images and memes, along with textual content, in order to increase the engagement and the credibility of their posts. In this paper, we present the first study of images shared by state-sponsored accounts by analyzing a ground truth dataset of 1.8M images…
Over the past few years, there is a heated debate and serious public concerns regarding online content moderation, censorship, and the principle of free speech on the Web. To ease these concerns, social media platforms like Twitter and Facebook refined their content moderation systems to support soft moderation interventions. Soft moderation interventions refer to warning labels attached to potentially questionable or harmful content to inform other users about the content and its nature while the content remains accessible, hence alleviating concerns related to censorship and free speech. In …
Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety …
