2,136 research outputs found
Social media mining under the COVID-19 context: Progress, challenges, and opportunities
Social media platforms allow users worldwide to create and share information, forging vast sensing networks that
allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the
COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges
from various perspectives. This review summarizes the progress of social media data mining studies in the
COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human
mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and
misinformation, and hatred and violence. We further document essential features of publicly available COVID-19
related social media data archives that will benefit research communities in conducting replicable and repro�ducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential
impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social
media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining
efforts in COVID-19 related studies and provides future directions along which the information harnessed from
social media can be used to address public health emergencies
An Exploratory Study of COVID-19 Misinformation on Twitter
During the COVID-19 pandemic, social media has become a home ground for
misinformation. To tackle this infodemic, scientific oversight, as well as a
better understanding by practitioners in crisis management, is needed. We have
conducted an exploratory study into the propagation, authors and content of
misinformation on Twitter around the topic of COVID-19 in order to gain early
insights. We have collected all tweets mentioned in the verdicts of
fact-checked claims related to COVID-19 by over 92 professional fact-checking
organisations between January and mid-July 2020 and share this corpus with the
community. This resulted in 1 500 tweets relating to 1 274 false and 276
partially false claims, respectively. Exploratory analysis of author accounts
revealed that the verified twitter handle(including Organisation/celebrity) are
also involved in either creating (new tweets) or spreading (retweet) the
misinformation. Additionally, we found that false claims propagate faster than
partially false claims. Compare to a background corpus of COVID-19 tweets,
tweets with misinformation are more often concerned with discrediting other
information on social media. Authors use less tentative language and appear to
be more driven by concerns of potential harm to others. Our results enable us
to suggest gaps in the current scientific coverage of the topic as well as
propose actions for authorities and social media users to counter
misinformation.Comment: 20 pages, nine figures, four tables. Submitted for peer review,
revision
Multimodal Automated Fact-Checking: A Survey
Misinformation is often conveyed in multiple modalities, e.g. a miscaptioned
image. Multimodal misinformation is perceived as more credible by humans, and
spreads faster than its text-only counterparts. While an increasing body of
research investigates automated fact-checking (AFC), previous surveys mostly
focus on text. In this survey, we conceptualise a framework for AFC including
subtasks unique to multimodal misinformation. Furthermore, we discuss related
terms used in different communities and map them to our framework. We focus on
four modalities prevalent in real-world fact-checking: text, image, audio, and
video. We survey benchmarks and models, and discuss limitations and promising
directions for future researchComment: The 2023 Conference on Empirical Methods in Natural Language
Processing (EMNLP): Finding
Building Credibility, Trust, and Safety on Video-Sharing Platforms
Video-sharing platforms (VSPs) such as YouTube, TikTok, and Twitch attract millions of users and have become influential information sources, especially among the young generation. Video creators and live streamers make videos to engage viewers and form online communities. VSP celebrities obtain monetary benefits through monetization programs and affiliated markets. However, there is a growing concern that user-generated videos are becoming a vehicle for spreading misinformation and controversial content. Creators may make inappropriate content for attention and financial benefits. Some other creators also face harassment and attack. This workshop seeks to bring together a group of HCI scholars to brainstorm technical and design solutions to improve the credibility, trust, and safety of VSPs. We aim to discuss and identify research directions for technology design, policy-making, and platform services for video-sharing platforms. © 2023 Owner/Author
Detecting Abusive Language on Online Platforms: A Critical Analysis
Abusive language on online platforms is a major societal problem, often
leading to important societal problems such as the marginalisation of
underrepresented minorities. There are many different forms of abusive language
such as hate speech, profanity, and cyber-bullying, and online platforms seek
to moderate it in order to limit societal harm, to comply with legislation, and
to create a more inclusive environment for their users. Within the field of
Natural Language Processing, researchers have developed different methods for
automatically detecting abusive language, often focusing on specific
subproblems or on narrow communities, as what is considered abusive language
very much differs by context. We argue that there is currently a dichotomy
between what types of abusive language online platforms seek to curb, and what
research efforts there are to automatically detect abusive language. We thus
survey existing methods as well as content moderation policies by online
platforms in this light, and we suggest directions for future work
Talking Abortion (Mis)information with ChatGPT on TikTok
In this study, we tested users' perception of accuracy and engagement with
TikTok videos in which ChatGPT responded to prompts about "at-home" abortion
remedies. The chatbot's responses, though somewhat vague and confusing,
nonetheless recommended consulting with health professionals before attempting
an "at-home" abortion. We used ChatGPT to create two TikTok video variants -
one where users can see ChatGPT explicitly typing back a response, and one
where the text response is presented without any notion to the chatbot. We
randomly exposed 100 participants to each variant and found that the group of
participants unaware of ChatGPT's text synthetization was more inclined to
believe the responses were misinformation. Under the same impression, TikTok
itself attached misinformation warning labels ("Get the facts about abortion")
to all videos after we collected our initial results. We then decided to test
the videos again with another set of 50 participants and found that the labels
did not affect the perceptions of abortion misinformation except in the case
where ChatGPT explicitly responded to a prompt for a lyrical output. We also
found that more than 60% of the participants expressed negative or hesitant
opinions about chatbots as sources of credible health information
SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice
To counter online abuse and misinformation, social media platforms have been
establishing content moderation guidelines and employing various moderation
policies. The goal of this paper is to study these community guidelines and
moderation practices, as well as the relevant research publications to identify
the research gaps, differences in moderation techniques, and challenges that
should be tackled by the social media platforms and the research community at
large. In this regard, we study and analyze in the US jurisdiction the fourteen
most popular social media content moderation guidelines and practices, and
consolidate them. We then introduce three taxonomies drawn from this analysis
as well as covering over one hundred interdisciplinary research papers about
moderation strategies. We identified the differences between the content
moderation employed in mainstream social media platforms compared to fringe
platforms. We also highlight the implications of Section 230, the need for
transparency and opacity in content moderation, why platforms should shift from
a one-size-fits-all model to a more inclusive model, and lastly, we highlight
why there is a need for a collaborative human-AI system
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