Study and analysis of Social network Aggregator

Virmani, Charu; Pillai, Anuradha; Juneja, Dimple

doi:10.1109/icroit.2014.6798314

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…This might potentially help: 1) determine links between groups of related users posting across different social media sites that might otherwise go unnoticed and 2) minimize data scarcity in the incoming social signals since a greater number of data samples from multiple social media portals can be obtained. Second, social media aggregation platforms or unified social media post crawlers, such as [109] and [110], can be incorporated in which posts from multiple social platforms by the same users along with their peers or people in their networks are correlated and scraped/crawled together [109]. Afterward, network analysis techniques, such as graph theory and link prediction, can be applied to the obtained data to reveal close contacts within users residing on different social networks, thereby reducing false negatives.…”

Section: B Data Collection Challengementioning

confidence: 99%

CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact Tracing

Rashid

Wang

2023

IEEE Trans. Comput. Soc. Syst.

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With the proliferation of smart devices and widespread Internet connectivity, social sensing is advancing as a pervasive sensing paradigm where experiences shared by individuals on social platforms (e.g., Twitter and Facebook) are analyzed to interpret the physical world. In this article, we introduce CovidTrak, a vision of social intelligence-empowered contact tracing that aims to scrutinize the knowledge derived using social sensing to track Coronavirus Disease 2019 (COVID-19) infections among the general public. Contact tracing is known to be an effective technique for detecting and monitoring persons who may have been exposed to individuals infected with any communicable disease. While a good number of contact tracing schemes are existent today (e.g., in-person and phone interviews, paper forms, email and web-based questionnaires, and smartphone apps), they often require active user participation and might miss certain cases of social interactions that go off-the-records but still lead to COVID-19 transmission. By contrast, social sensing provides an alternative avenue for spontaneously determining such contacts by harnessing the rich experiences and information conveyed by people on social data platforms (e.g., a group photograph tweeted from a house party with a potential contact). As such, CovidTrak can form a powerful basis to combat the COVID-19 pandemic. The vision of CovidTrak intends to answer the following questions: 1) how to bolster the privacy and security of the online users while determining their contacts? 2) how to collect relevant social signals that indicate in-person encounters among people? 3) how to reliably process the vast amount of noisy data from social platforms to identify chains of transmission? 4) how to handle the scarcity of location metadata in the incoming data? 5) how to effectively communicate crucial contact information to concerned individuals? and 6) how to model and handle the responses of the common people toward contact information? We envision unexplored opportunities to leverage multidisciplinary techniques to address the above questions and develop effective future CovidTrak schemes.

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Section: B Data Collection Challengementioning

confidence: 99%

CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact Tracing

Rashid

Wang

2023

IEEE Trans. Comput. Soc. Syst.

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“…Some studies employ automated script that automatically scans and crawls content from websites using HTTP requests/responses [14]. Other researchers collect data through a social network aggregator [14,18] or by tracing network traffic from Internet service providers (ISPs) [16]. However, some of the data available on SNSs cannot be collected with this approach.…”

Section: ) Sns Measurementmentioning

confidence: 99%

“…Researchers have studied it by collecting the data of SNS usage behaviors as a first step [9,[14][15][16][17][18]. Many types of data and collection methods exist.…”

Section: Data Collectionmentioning

confidence: 99%

Analysis of Questionnaires on User Behavior for Assessing Symptoms of Excessive SNS Usage

Intapong

Charoenpit

Achalakul

et al. 2018

IJAE

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LITERATURE SURVEY Negative consequences of excessive SNS usageMany studies have argued that excessive SNS usage causes various negative consequences. People who spend too much time on SNSs are less involved in their real life communities [1]. Students who use Facebook have lower grades than those who do not because the former are easily distracted and exercise poor time management [1]. J. concluded that Kuwaiti university students who spend time on SNSs instead of studying have lower grades. In Thailand, many teenagers suffer from such negative effects of excessive SNS usage as lack of sleep, reduced academic performance, inappropriate manners, negative emotional expressions, impairment of family and social functions, and mental health problems [5]. Excessive SNS usage delays bedtimes and reduces sleep quality [6].Regarding those research outcomes, excessive SNS usage causes relational, performance, health-related, and emotional problems, including the risk of addiction [1]. SNS addictionThe excessive and compulsive use of SNSs has been linked to behavioral addictions [6]. SNS addiction also shares similarities with other behavioral addictions [1,6] Technology, Tokyo, Japan Abstract: The use of social networking sites (SNSs) continues to dramatically increase. People are spending unexpected and unprecedented amounts of time online. Such often excessive and compulsive use has been categorized as a behavioral addiction. This research is conducted to assess the symptoms of excessive SNS usage by studying user behavior in SNSs. We designed and developed a data collection application as a tool for gathering data from questionnaires and SNSs by APIs from 177 volunteers at the Thai-Nichi Institute of Technology (TNI), Thailand. This paper introduces the analysis results of questionnaire data to find the factors associated with SNS addiction. Our analytic results identified potential candidates for the key components of SNS addiction.

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“…T.Nithya [3] A description over web structure mining is presented. It is the process of extracting useful information from the web to provide better performance to search data.…”

Section: Related Workmentioning

confidence: 99%

Diversified and Scalable Recommendation System for the Web Services

2017

IJAERD

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Study and analysis of Social network Aggregator

Cited by 11 publications

References 8 publications

CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact Tracing

CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact Tracing

Analysis of Questionnaires on User Behavior for Assessing Symptoms of Excessive SNS Usage

Diversified and Scalable Recommendation System for the Web Services

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