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2022 ◽  
Vol 3 (2) ◽  
pp. 120-125
Author(s):  
Almighty C. Tabuena

The establishment of the K-12 curriculum has had a significant impact on subject requirements related to the outcome-based education plan and the requisite output for a given research report or requirement. Social networking platforms enable students to effortlessly complete a variety of tasks, such as learning and performance. By intervening in research, social networking sites break down the barriers that limit both students and teachers in the research process. Three methodologies or ideas have arisen, known as approaches, that could help you facilitate teaching research, even if you are not in the research discipline: the Facebook-Personality Network Approach, the Virtual Research Journal, and the Google Immersion Approach. It is considered favorably by some students and users, but there are those who take advantage of its negative aspects. Instead of focusing on the emerging ideas or topics created by coding, I used social networking sites to demonstrate that research can be done anytime, anyplace, for any purpose or cause. According to the outcome-based education paradigm, students found the three techniques highly engaging. In order to be a teacher-researcher, you must utilize your originality and resourcefulness when it comes to all of the resources, devices, and technology, as well as the available social networking sites.


2022 ◽  
Vol 951 (1) ◽  
pp. 012026
Author(s):  
Muslikhin Muslikhin ◽  
L W Evelina ◽  
M Rizkiansyah ◽  
I Prawira ◽  
R E Irawan

Abstract The study aims to reveal the communication network on Twitter about forest fires in Indonesia during the COVID-19 pandemic. The research method used is descriptive qualitative. The theory used in this research is communication networking. The analysis technique uses Social Networking Analysis or SNA. Conversation data on Twitter is pulled through emprit drones by entering keywords fire, forest, and land. The research findings show; Conversation volume was 617 conversations, with five actors mentioning the most, namely @VICE-ID, @BBCIndonesia, @totoroeksib, @GoldenMiru, and @ari_trismana with 16 conversations. Of the 617 conversations, 243 had negative sentiments, 368 had positive sentiments and 6 were neutral. The five words that appear the most in the discussion of forest and land fires are forest - hutan (with a small h) 459 times, land -lahan 147 times, Forest -Hutan (with large H) 80 times, disaster -bencana 60 times, and smoke -asap 56 times. During the 10 days of talking, there were 67 hashtags, with the top five being #hijabbugil, #eksibisionis, and # exhibition with 27 mentions, following #PreventedKarhutla with 16 mentions, and 13 mentions #KoperasiPerkuatPertuangan.


Author(s):  
Shivam Puri ◽  
Sukhpreet Kaur

There are several interconnected entities present within the networked data for which the generation of inferences is important. For instance, hyperlinks are used to interconnect the web pages, calls are used to link the phone accounts, and references are used to connect the research papers and so on. Almost every existing application includes networks within it. The daily lives of individuals include social networking, making financial transactions, generating networks that show physical systems and so on. The manner in which the nodes present within the system influence each other can be known through this research. On the basis of observed attributed of an object within the system, another attributed is predicted using new model. The various network traffic classification techniques are reviewed in terms of certain parameters.


Author(s):  
Hamed Qahri-Saremi ◽  
Isaac Vaghefi ◽  
Ofir Turel

Prior studies have primarily used "variable-centered" perspectives to identify factors underlying user responses to social networking site (SNS) addiction, their predictors and outcomes. This paper extends this perspective by taking a person-centered approach to examine (1) the prototypical subpopulations (profiles) of users' extent of SNS addiction and responses to it, (2) how affiliations with these profiles can explain user behaviors toward SNS use, and (3) how personality traits can predict affiliations with these profiles. To this end, we propose a typological theory of SNS addiction and user responses to it via two empirical, personcentered studies. Study 1 draws on survey data from 188 SNS users to develop a typology of users based on the extent of their SNS addiction and their responses to it. It further examines the relations between affiliation with these profiles and users' SNS discontinuance intention, as a typical behavioral response to SNS addiction. Study 2 uses survey data from 284 SNS users to validate the user typology developed in Study 1 and investigate its relations to users' Big Five personality traits. Our findings shed light on a typology of five prototypical profiles of SNS users-cautious, regular, consonant, dissonant, and hooked-who differ in their extent of SNS addiction and their cognitive, emotional, and behavioral responses to it. Our findings also demonstrate how Big Five personality traits can predict user affiliations with these prototypical profiles.


Author(s):  
Valliyammai Chinnaiah ◽  
Cinu C Kiliroor

Spam is an undesirable content that present on online social networking sites, while spammers are the users who post this content on social networking sites. Unwanted messages posted on Twitter may have several goals and the spam tweets can interfere with statistics presented by Twitter mining tools and squander users’ attention.. Since Twitter has achieved a lot of attractiveness through-out the world, the interest towards it by the spammers and malevolent users is also increases. To overcome the spam problems many researchers proposed ideas using machine learning algorithms for the identification of spam messages. Not only the selection of classifiers but also the variegated feature analysis is essential for the identification of irrelevant messages in social networks. The proposed model performs a heterogeneous feature analysis on the twitter data streams for classifying the unsolicited messages using binary and continuous feature extraction with sentiment analysis on social network datasets. The features created are assessed using significant stratagems and the finest features are selected. A classifier model is built using these feature vectors to predict and identify the spam messages in Twitter. The experimental results clearly show that the proposed Sentiment Analysis based Binary and Continuous Feature Extraction model with Random Forest (SA-BC-RF) approach classifies the spam messages from the social networks with an accuracy of 90.72% when compared with the other state-of-the-art methods.


Author(s):  
Marta Tremolada ◽  
Lucio Silingardi ◽  
Livia Taverna

The evolution of digital media in adolescents has changed the patterns and motives of use and the impact on their communication choices in their social and family networks. The objectives of this study are to understand how peers communicate adopting a social network (SN) or by voice and their social desirability. After the informant consent signature, the adolescents completed a series of self-report questionnaires on the use of SN, on communication preferences, and on social desirability through online. Most of the adolescents belonged to the 17-19 age group (83.6%) and were female (68.9%). Adolescents spent more than 3 hours/day on Whatsapp and more than 2 hours/day on Instagram, while the use of Facebook was on average only 35 minutes/day. Females used digital media for longer than males. Adolescents aged 17-19 years choose more Facebook and voice modes compared to adolescents aged 14 and 16 years. The alternative modes of Whatsapp and voice were chosen more than the social networks in their communication strategies, especially for negative topics. Motives for use were, in addition to boredom, related to maintaining one's social sphere with peers. Some educative considerations were made based on these results.


2021 ◽  
Author(s):  
Nishchal J

Every person has an equal right to information, therefore, impairments shouldn’t restrict people from gaining this knowledge from any form of source. Social Networking applications have tremendously grown their popularity among all kinds of age groups for providing socialising opportunities, entertainment and exchange of knowledge. Hence, the motive of this paper is to propose a social networking application pipeline with a strong Machine Learning backend which makes it more accessible to the blind, deaf and dumb section of the society who otherwise do not enjoy the features of social networking platforms.


2021 ◽  
Author(s):  
Nishchal J

Every person has an equal right to information, therefore, impairments shouldn’t restrict people from gaining this knowledge from any form of source. Social Networking applications have tremendously grown their popularity among all kinds of age groups for providing socialising opportunities, entertainment and exchange of knowledge. Hence, the motive of this paper is to propose a social networking application pipeline with a strong Machine Learning backend which makes it more accessible to the blind, deaf and dumb section of the society who otherwise do not enjoy the features of social networking platforms.


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