"Learn the Facts About COVID-19": Analyzing the Use of Warning Labels on TikTok Videos

During the COVID-19 pandemic, health-related misinformation and harmful content shared online had a significant adverse effect on society. To mitigate this adverse effect, mainstream social media platforms employed soft moderation interventions (i.e., warning labels) on potentially harmful posts. Despite the recent popularity of these moderation interventions, we lack empirical analyses aiming to uncover how these warning labels are used in the wild, particularly during challenging times like the COVID-19 pandemic. In this work, we analyze the use of warning labels on TikTok, focusing on COVID-19 videos. First, we construct a set of 26 COVID-19 related hashtags, then we collect 41K videos that include those hashtags in their description. Second, we perform a quantitative analysis on the entire dataset to understand the use of warning labels on TikTok. Then, we perform an in-depth qualitative study, using thematic analysis, on 222 COVID-19 related videos to assess the content and the connection between the content and the warning labels. Our analysis shows that TikTok broadly applies warning labels on TikTok videos, likely based on hashtags included in the description. More worrying is the addition of COVID-19 warning labels on videos where their actual content is not related to COVID-19 (23% of the cases in a sample of 143 English videos that are not related to COVID-19). Finally, our qualitative analysis on a sample of 222 videos shows that 7.7% of the videos share misinformation/harmful content and do not include warning labels, 37.3% share benign information and include warning labels, and that 35% of the videos that share misinformation/harmful content (and need a warning label) are made for fun. Our study demonstrates the need to develop more accurate and precise soft moderation systems, especially on a platform like TikTok that is extremely popular among people of younger age

``Learn the Facts About {COVID-19}'': {A}nalyzing the Use of Warning Labels on {TikTok} Videos

During the COVID-19 pandemic, health-related misinformation and harmfulcontent shared online had a significant adverse effect on society. To mitigatethis adverse effect, mainstream social media platforms employed soft moderationinterventions (i.e., warning labels) on potentially harmful posts. Despite therecent popularity of these moderation interventions, we lack empirical analysesaiming to uncover how these warning labels are used in the wild, particularlyduring challenging times like the COVID-19 pandemic. In this work, we analyzethe use of warning labels on TikTok, focusing on COVID-19 videos. First, weconstruct a set of 26 COVID-19 related hashtags, then we collect 41K videosthat include those hashtags in their description. Second, we perform aquantitative analysis on the entire dataset to understand the use of warninglabels on TikTok. Then, we perform an in-depth qualitative study, usingthematic analysis, on 222 COVID-19 related videos to assess the content and theconnection between the content and the warning labels. Our analysis shows thatTikTok broadly applies warning labels on TikTok videos, likely based onhashtags included in the description. More worrying is the addition of COVID-19warning labels on videos where their actual content is not related to COVID-19(23% of the cases in a sample of 143 English videos that are not related toCOVID-19). Finally, our qualitative analysis on a sample of 222 videos showsthat 7.7% of the videos share misinformation/harmful content and do not includewarning labels, 37.3% share benign information and include warning labels, andthat 35% of the videos that share misinformation/harmful content (and need awarning label) are made for fun. Our study demonstrates the need to developmore accurate and precise soft moderation systems, especially on a platformlike TikTok that is extremely popular among people of younger age.<br

Search Bias Quantification: Investigating Political Bias in Social Media and Web Search

Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.publishe

Glimmers: Resolving the Privacy/Trust Quagmire

Many successful services rely on trustworthy contributions from users. To establish that trust, such services often require access to privacy-sensitive information from users, thus creating a conflict between privacy and trust. Although it is likely impractical to expect both absolute privacy and trustworthiness at the same time, we argue that the current state of things, where individual privacy is usually sacrificed at the altar of trustworthy services, can be improved with a pragmatic $Glimmer$ $of$ $Trust$, which allows services to validate user contributions in a trustworthy way without forfeiting user privacy. We describe how trustworthy hardware such as Intel's SGX can be used client-side -- in contrast to much recent work exploring SGX in cloud services -- to realize the Glimmer architecture, and demonstrate how this realization is able to resolve the tension between privacy and trust in a variety of cases

A REVIEW ON PALMYRA PALM (BORASSUS FLABELLIFER)

The medicinal plants have very important role in the health of human beings as well as animals. India is the largest producer of medicinal plants. One such plant, Borassus flabellifer L, belongs to family Arecaceae, commonly known as Palmyra palm is a native of tropical Africa but cultivated throughout India. Traditionally the different parts of the plant such as root, leaves, fruit, and seeds are used for various human disorders. Leaves are used forÂ&nbsp;thatching,Â&nbsp;mats,Â&nbsp;baskets,Â&nbsp;fans. Flowers of B. flabellifer were investigated for analgesic and antipyretic effects, anti-inflammatory activity, haematological, biochemical parameters, and immunosuppressant property. The different parts of the plant are being used for medicinal properties like antihelminthic and diuretic. The fruit pulp of B. flabellifer has been used in traditional dishes and the sap, has been used as a sweetener for diabetic patients. Phytochemical studies of the plant revealed the presence of spirostane-type steroid saponins; steroidal glycoside also contains a bitter compound called flabelliferrins. Although investigations have been carried out a lot more can still be explored, exploited and utilized. The present review highlights the phytochemical and pharmacological studies including folklore medicinal uses of this plant

On Compact Routing for the Internet

While there exist compact routing schemes designed for grids, trees, and Internet-like topologies that offer routing tables of sizes that scale logarithmically with the network size, we demonstrate in this paper that in view of recent results in compact routing research, such logarithmic scaling on Internet-like topologies is fundamentally impossible in the presence of topology dynamics or topology-independent (flat) addressing. We use analytic arguments to show that the number of routing control messages per topology change cannot scale better than linearly on Internet-like topologies. We also employ simulations to confirm that logarithmic routing table size scaling gets broken by topology-independent addressing, a cornerstone of popular locator-identifier split proposals aiming at improving routing scaling in the presence of network topology dynamics or host mobility. These pessimistic findings lead us to the conclusion that a fundamental re-examination of assumptions behind routing models and abstractions is needed in order to find a routing architecture that would be able to scale ``indefinitely.''Comment: This is a significantly revised, journal version of cs/050802