Facebook App Research Articles

Influence of Facebook Use on the Study Habits of Selected Grade 7 Learners in Mindanao State University - University Training Center

The study aimed to investigate the influence of using Facebook application on the study habits of selected fifty (50) Grade 7 learners in Mindanao State University- University Training Center, school year 2021-2022. The study adapted descriptive research design using survey-questionnaire to help researchers gain relevant information quickly and effectively. The instrument has two (2) parts: part one, the personal profile of the respondents, such are: age, gender, parents’ occupation and parents’ monthly income and part two, the perceptions on the influence of using Facebook application on the learners’ study habits. The instrument has twenty-two (22) items, and the respondents was given choices to answer using a 4-point Likert scales such Strongly Disagree=4, Disagree = 3, Agree = (2) Strongly Agree = 1. The gathered data result was treated using the statistical tools such are: frequency, percentage, mean and standard deviation using a Statistical Product and Service Solutions SPSS) software. The statistical tools used are vital that enabling researchers to gain meaningful insights and interpret the results adequately. Moreover, based on the findings of the study, the demographic profile of the respondents revealed that 52% of the respondents were belonged to the age bracket of 12 to 15 years old, 72% were female. Additionally, many or 36% of the respondents’ mothers’ occupation were engaged in business, likewise 44% of the respondents’ fathers’ occupation were engaged in small businesses too. Moreover, on the respondents’ parents’ monthly income 54% of them assessed that around Php 5,001.00 to 10,000.00 as their parents’ monthly income, and 72 % had one to two Facebook accounts registered and 72% answered that they spent three to five hours using Facebook application. Meanwhile, the results also manifest that frequent used of Facebook application affects learners’ study habit. The data showed average weighted mean is 2.568 and descriptively defined as “agree”, this connotes that respondents use Facebook application for fun and popularity. This signified that the use of Facebook app affected the study habits of the learners. Thus, the major implication derived from the higher addiction to Facebook, where the study habit become poor and academic achievement reduced resulting to lower grades. It is highly recommended that to the learners to use Facebook as a tool for studying rather than using it unnecessarily. Likewise, the Facebook addiction resulted to learner-respondents’ lower academic performance. Anent to this, the parents should set rules in using online applications.

When Facebook Is the Internet: The Role of Social Media in Ethnic Conflict

This paper investigates whether social media access is associated with increased probability or intensity of ethnic conflict in Myanmar. In this context most people use mobile phones, and particularly the Facebook app, to access the internet. To distinguish the effects of social media from those of the broader internet, I exploit geographic variation in mobile phone coverage as a proxy for Facebook availability. Despite evidence of a hate-campaign utilizing Facebook to reach wide audiences, I do not find that social media access is associated with increased probability or intensity of conflict. The only exception to the null result is variation related to the Rohingya crisis: in this regional setting suggestive evidence points to Facebook availability being associated with slightly higher probability of conflict.

Literatur Review :Analisis Mengenai Facebook

Abstrack Facebook has far more than 2.91 billion users worldwide, as of October 2021 by following its commitment to “Give people the power to build community and bring the world closer together”. May of Facebook, major acquisition of Facebook, applications of AI in Facebook and Financial plans and challenges. The entire Facebook company will now be known as Meta. The Facebook app, on the other hand, will keep its name, and other apps will not be affected. Facebook is now known as 'Meta' because of its new focus which means 'after' or 'beyond' in Greek. However, Instagram, Facebook, WhatsApp, and Messenger are now all part of the 'Meta' company, much like Google's products are all part of the Alphabet company. Artificial intelligence, AI is really essential to Facebook. Keyword: Facebook, Meta, Machine learning

OPEN DATA CUBE APPLICATION TO USER-GENERATED GEODATA: VISITORS TURNOUT INVESTIGATION IN THE INSUBRIA NATURAL PARKS

Abstract. Green areas such as natural and periurban parks embed key assets for biodiversity and landscape preservation and environmental education. In most cases, they also boost local economic growth for their hosting territories thanks to the establishment of eco-tourism activities. Nevertheless, coordinated management and promotion actions between policymakers and promoters remain vital for the sustainable exploitation of these green areas. The knowledge of visitors dynamics across natural parks is necessary information necessary to drive parks management policies while being often a complex data to collect. To that end, this paper proposes the use of user-generated geodata, namely users’ location records provided by the Facebook Data for Good program, to assess visitors’ turnout for the natural parks of the Insubria Region, between Southern Switzerland and Northern Italy. The Open Data Cube technology, originally developed for managing satellite Earth observation data, was here adapted for processing and analysing the considered user-generated geodata. Space-time patterns of Facebook users’ presence in the period May 2020 – December 2021 were extracted to infer visitors’ fluxes and destinations preferences. Results pointed out differences between Italian and Swiss parks by outlining also most visited locations within each park. Despite limitations related to data representativeness (limited sample of Facebook app users with location history enabled on their devices) the integration of user-generated geodata in a cutting-edge and free and open-source data management platform, such as the Open Data Cube, turned out to be promising for the improvement of natural areas management practices.

An Overview of Facebook’s Journey to Meta - A Case Study

Purpose: Facebook has far more than 2.91 billion users worldwide, as of October 2021 by following its commitment to “Give people the power to build community and bring the world closer together”. Machine learning as well as applied Machine learning helps people to get new contents and related stories. Machine learning (ML) is a technique for recognizing and drawing conclusions from data connections. Speech recognition systems can caption videos on Facebook using machine learning, making them more accessible. The Facebook research team is working with Machine learning technology to give its users the best services. Here an analysis has been done on the history of Facebook, major acquisition of Facebook, applications of AI in Facebook and Financial plans and challenges. The entire Facebook company will now be known as Meta. The Facebook app, on the other hand, will keep its name, and other apps will not be affected. Facebook is now known as 'Meta' because of its new focus which means 'after' or 'beyond' in Greek. However, Instagram, Facebook, WhatsApp, and Messenger are now all part of the 'Meta' company, much like Google's products are all part of the Alphabet company. Artificial intelligence, AI is really essential to Facebook. Approach: For this analysis, a sophisticated survey strategy based on secondary data was applied. Findings: Based on the findings, it is clear that to provide high-quality service, Facebook uses leading edge artificial intelligence/machine learning technologies. Originality: To identify the methodologies employed in the organization's services, a study is conducted based on the existing resources. Paper Type: Descriptive Case Study Research

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Habitual entrepreneurship in digital platform ecosystems: A time-contingent model of learning from prior software project experiences

The emergence of large-scale digital platforms such as Facebook, Google Play and Apple App Store around 2008 has created opportunities for independent entrepreneurs to offer their self-developed software applications (“apps”) to large groups of platform users. The development and release of tens of thousands of apps by thousands of independent developers has created dynamic entrepreneurial ecosystems. This paper investigates whether and how learning by independent habitual entrepreneurs unfolds in substantively different ways in such dynamic platform-based environments. We argue that in these entrepreneurial ecosystems, the timing of learning efforts becomes essential. For Facebook app developers, we find that learning from their own prior app projects remains feasible. However, entrepreneurs have only a few months during which they can benefit from what they learned from a prior app project. This study supports the feasibility of time-contingent learning from prior app projects for increasingly prevalent dynamic entrepreneurial ecosystems such as digital platforms. Implications for future research and management practice are outlined.

Smartphone platforms as privacy regulators

A series of recent developments highlight the increasingly important role of online platforms in impacting data privacy in today's digital economy. Revelations and parliamentary hearings about privacy violations in Facebook's app and service partner ecosystem, EU Court of Justice judgments on joint responsibility of platforms and platform users, and the rise of smartphone app ecosystems where app behaviour is governed by app distribution platforms and operating systems, all show that platform policies can make or break the enjoyment of privacy by users. In this article, we examine these developments and explore the question of what can and should be the role of platforms in protecting data privacy of their users.The article first distinguishes the different roles that platforms can have in ensuring respect for data privacy in relevant ecosystems. These roles include governing access to data, design of relevant interfaces and privacy mechanisms, setting of legal and technical standards, policing behaviour of the platform's (business) users, coordinating responsibility for privacy issues between platform users and the platform, and direct and indirect enforcement of a platform's data privacy standards on relevant players. At a higher level, platforms can also perform a role by translating different international regulatory requirements into platform policies, thereby facilitating compliance of apps in different regulatory environments. And in all of this, platforms are striking a balance between ensuring the respect for data privacy in data-driven environments on the one hand and optimization of the value and business opportunities connected to the platform and underlying data for users of the platform on the other hand.After this analysis of platforms’ roles in protecting privacy, the article turns to the question of what should this role be and how to better integrate platforms in the current legal frameworks for data privacy in Europe and the US. The article will argue for a compromise between direct regulation of platforms and mere self-regulation, in arguing that platforms should be required to make official disclosures about their privacy-related policies and practices for their respective ecosystems. These disclosures should include statements about relevant conditions for access to data and the platform, the platform's standards with respect to privacy and the way in which these standards ensure or facilitate compliance with existing legal frameworks by platform users, and statements with respect to the risks of abuse of different data sources and platform tools and actions taken to prevent or police such abuses. We argue that such integration of platforms in current regulatory frameworks is both feasible and desirable. It would make the role that platforms already have in practice more explicit. This would help to highlight best practices, create more accountability and could save significant regulatory and compliance resources in bringing relevant information together in one place. In addition, it could provide clarity for business users of platforms, who are now sometimes confronted with restrictive decisions by platforms in ways that lack transparency and oversight.

Relationships between Local Green Space and Human Mobility Patterns during COVID-19 for Maryland and California, USA

Human mobility is a significant factor for disease transmission. Little is known about how the environment influences mobility during a pandemic. The aim of this study was to investigate an effect of green space on mobility reductions during the early stage of the COVID-19 pandemic in Maryland and California, USA. For 230 minor civil divisions (MCD) in Maryland and 341 census county divisions (CCD) in California, we obtained mobility data from Facebook Data for Good aggregating information of people using the Facebook app on their mobile phones with location history active. The users’ movement between two locations was used to calculate the number of users that traveled into an MCD (or CCD) for each day in the daytime hours between 11 March and 26 April 2020. Each MCD’s (CCD’s) vegetation level was estimated as the average Enhanced Vegetation Index (EVI) level for 1 January through 31 March 2020. We calculated the number of state and local parks, food retail establishments, and hospitals for each MCD (CCD). Results showed that the daily percent changes in the number of travels declined during the study period. This mobility reduction was significantly lower in Maryland MCDs with state parks (p-value = 0.045), in California CCDs with local-scale parks (p-value = 0.048). EVI showed no association with mobility in both states. This finding has implications for the potential impacts of green space on mobility under an outbreak. Future studies are needed to explore these findings and to investigate changes in health effects of green space during a pandemic.

Short communication: Differences in local perceptions of Osteochilus spilurus (Cyprinidae: Labeoninae) from several islands in Indonesia

Abstract. Kurniawan A, Pramono DY, Indrayati A, Hermanto, Triswiyana I. 2020. Short communication: Differences in local perceptions of Osteochilus spilurus (Cyprinidae: Labeoninae) from several islands in Indonesia. Asian J Ethnobiol 3: 79-84. Osteochilus spilurus is native freshwater fish on the islands of Sundaland, Indonesia. No study has been reported on this fish utilization other than in the Belitung Islands, so another local perception of Indonesian island needs to be investigated. Local recreational fishing in Palangkaraya, Pontianak, and Pekanbaru and fishers in Palangkaraya, Eastern and western of Belitung, Southern and Central Bangka, and North Lampung were the sources of local knowledge. We obtained data using a Facebook app survey to see local recreational fishers' awareness, interviews with one fisherman in each region, and a literature review for Belitung public perception. There is a different awareness of people from East Belitung to other regions. In East Belitung, knowledge of O. spilurus has a link to the local culture. Fishermen have the most relevant fishing gear expertise for the catch of O. spilurus, based on environmental factors and fish behavior. Large-scale fishing, consuming, and trade of it only takes place in East Belitung. It has an impact on the use of non-environmentally sustainable mesh size nets that threaten their natural survival.

Relationships between Local Vegetation Level and Human Mobility Patterns during COVID-19 for Maryland, USA

Background: Human mobility is a significant factor for disease transmission. Little is known about if built environment influence mobility during the COVID-19 pandemic. We investigated a potential effect of local vegetation level on mobility reductions during the COVID-19 pandemic in Maryland, USA. Methods: Our study regions included 193 minor civil divisions (MCDs) in Maryland. We obtained mobility data from Facebook Data for Good, which aggregate information of people using Facebook app on their mobile phones with location history active. The user’s movement between two locations during a given time window at the 4.9km by 4.9km spatial scale was used to calculate the number of users traveled into a region for each day between March 11, 2020 and April 26, 2020. The mobility change for a region for each day was estimated as percent change in the number of users traveling into that region in the daytime (8am - 4pm) compared to the average number of users traveled into that region during the same daytime window and the same day of the week between February 24, 2020 and March 10, 2020. Each MCD’s vegetation level was estimated as the average Enhanced Vegetation Index level through January 1, 2020 and March 31, 2020. Results: Among 193 study regions, user’s movement were observed in 53 regions, which had higher population and percent impervious area. The daily percent changes in the number of users traveling into those 53 regions declined during the study period. This mobility reduction was significantly higher in regions with the lowest level of EVI indicating lower vegetation level (p-value=0.041). Conclusions: Mobility reduction during the COVID-19 pandemic in Maryland was less in regions with higher vegetation level. Future studies will need to investigate changes in health effects of green space during a pandemic and who are disproportionately affected by such changes.

Intelligent and adaptive tutoring through a social network for higher education

ABSTRACT Social Media have become a tool for communication among people, the majority of whom belong to the younger generation. Hence, exploiting them in the educational field can promote communication and collaboration among students and instructors. Moreover, expanding them with more pedagogical tools, such as assessments and adaptivity, can improve further the learning outcomes. In view of the above, this paper presents i-LearnC#, an intelligent tutoring application over Facebook, for learning programming. This Facebook app extends the capabilities of Facebook platform to be used as an integrated tutoring system. In particular, i-LearnC# embodies an intelligent Virtual Coach offering individualised advice, covering students’ knowledge gaps and solving queries on unknown learning concepts. Regarding students’ assessment, i-LearnC# applies the Revised Bloom Taxonomy and delivers personalised assistance to them through the Virtual Coach. Finally, i-LearnC# employs cluster analysis for recommending the optimal groups for collaboration to students. i-LearnC# was evaluated using a well-known framework, the statistical hypothesis test and system's log files. The results are very promising, showing that i-LearnC# has been educationally beneficial to students in higher education promoting effective learning and teaching of computing education and achieving high acceptance rate as an adaptive intelligent social learning environment.

Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK

Background: Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which have resulted in reduced mobility across different regions. Crowd level data on mobile phone usage can be used as a proxy for actual population mobility patterns and provide a way of quantifying the impact of social distancing measures on changes in mobility. Methods: Here, we use two mobile phone-based datasets (anonymised and aggregated crowd level data from O2 and from the Facebook app on mobile phones) to assess changes in average mobility, both overall and broken down into high and low population density areas, and changes in the distribution of journey lengths. Results: We show that there was a substantial overall reduction in mobility, with the most rapid decline on the 24th March 2020, the day after the Prime Minister’s announcement of an enforced lockdown. The reduction in mobility was highly synchronized across the UK. Although mobility has remained low since 26th March 2020, we detect a gradual increase since that time. We also show that the two different datasets produce similar trends, albeit with some location-specific differences. We see slightly larger reductions in average mobility in high-density areas than in low-density areas, with greater variation in mobility in the high-density areas: some high-density areas eliminated almost all mobility. Conclusions: These analyses form a baseline from which to observe changes in behaviour in the UK as social distancing is eased and inform policy towards the future control of SARS-CoV-2 in the UK.

Information privacy behavior in the use of Facebook apps: A personality-based vulnerability assessment

The unauthorized use of personal information belonging to users of apps integrated with the Facebook platform affects millions of users. Crucially, although privacy concerns and awareness have increased, the use of these apps, and related privacy behaviors, remain largely unchanged. Given that such privacy behaviors are likely influenced by individuals' personality traits, it is imperative to better understand which personality traits make individuals more vulnerable to such unauthorized uses. We build on a recontextualized version of the theory of planned behavior (TPB) to evaluate the influence of the Big Five personality traits on attitudes toward Facebook privacy settings, social norms, and information privacy concerns (IPCs)—all within the context of Facebook app use. To evaluate this study's model, we analyzed 576 survey responses by way of partial least squares path modeling. Results indicate that highly extraverted individuals are particularly vulnerable to privacy violations (e.g., unauthorized use of personal information) because of their negative attitudes toward Facebook privacy settings. Our post hoc analysis uncovered interesting combinations of personality traits that make individuals particularly vulnerable to the unauthorized use of app-based information. In particular, the combination of extraversion and conscientiousness had a negative effect on individuals' attitude toward privacy settings. We also found a significant negative relationship between IPCs and intention to use Facebook apps. Finally, we found a positive relationship between social norms and intentions. Taken together, these results infer that individuals are likely to be influenced by their peers in the use of Facebook apps but that their intentions to use these apps declines as privacy concerns increase.

Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK.

Background: Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which have resulted in reduced mobility across different regions. Crowd level data on mobile phone usage can be used as a proxy for actual population mobility patterns and provide a way of quantifying the impact of social distancing measures on changes in mobility. Methods: Here, we use two mobile phone-based datasets (anonymised and aggregated crowd level data from O2 and from the Facebook app on mobile phones) to assess changes in average mobility, both overall and broken down into high and low population density areas, and changes in the distribution of journey lengths. Results: We show that there was a substantial overall reduction in mobility, with the most rapid decline on the 24th March 2020, the day after the Prime Minister's announcement of an enforced lockdown. The reduction in mobility was highly synchronized across the UK. Although mobility has remained low since 26th March 2020, we detect a gradual increase since that time. We also show that the two different datasets produce similar trends, albeit with some location-specific differences. We see slightly larger reductions in average mobility in high-density areas than in low-density areas, with greater variation in mobility in the high-density areas: some high-density areas eliminated almost all mobility. Conclusions: These analyses form a baseline from which to observe changes in behaviour in the UK as social distancing is eased and inform policy towards the future control of SARS-CoV-2 in the UK.

Dysfunction of Self-Control in Facebook Addiction: Impulsivity Is the Key

Facebook is one of the most popular social network sites and communication platforms. However, besides many positive elements related to the use of this network site, in some cases it may lead to addiction. Therefore, the main aim of our study was to identify Facebook addiction predictors, in particular, to verify whether impulsivity, as a dimension of self-control, is an important predictor of this type of addiction. We also examined whether Facebook addiction predictors such as time spent using Facebook, use of Facebook smartphone apps, state orientation and female gender would be significant in our model of Facebook addiction. The 234 participants in the study were assessed using the Facebook Intrusion Questionnaire, the Brief Self-Control Scale and the Action Control Scale. Impulsivity as a dimension of self-control, action control, amount of time spent on Facebook, Facebook app use and gender were found to be related to Facebook addiction. Specifically, a high level of impulsivity, more time spent using Facebook, female gender and Facebook smartphone app use are predictors of Facebook addiction. However, the relation between state orientation, restraint as a dimension of self-control and Facebook addiction was insignificant. Our results may indicate the role of impulsivity as a dimension of self-control in Facebook addiction. In addition, they may suggest that self-control should be taken into account not only as a one-dimensional but also as a multidimensional construct in Facebook addiction research. Our findings may also contribute to the better preparation of prevention and therapeutic programmes for people at risk of Facebook addiction.

Online social network security awareness: mass interpersonal persuasion using a Facebook app

Purpose Online social network (OSN) users have a high propensity to malware threats due to the trust and persuasive factors that underpin OSN models. The escalation of social engineering malware encourages a growing demand for end-user security awareness measures. The purpose of this paper is to take the theoretical cybersecurity awareness model TTAT-MIP and test its feasibility via a Facebook app, namely social network criminal (SNC). Design/methodology/approach The research employs a mixed-methods approach to evaluate the SNC app. A system usability scale measures the usability of SNC. Paired samples t-tests were administered to 40 participants to measure security awareness – before and after the intervention. Finally, 20 semi-structured interviews were deployed to obtain qualitative data about the usefulness of the App itself. Findings Results validate the effectiveness of OSN apps utilising a TTAT-MIP model – specifically the mass interpersonal persuasion (MIP) attributes. Using TTAT-MIP as a guidance, practitioners can develop security awareness systems that better leverage the intra-relationship model of OSNs. Research limitations/implications The primary limitation of this study is the experimental settings. Although the results testing the TTAT-MIP Facebook app are promising, these were set under experimental conditions. Practical implications SNC enable persuasive security behaviour amongst employees and avoid potential malware threats. SNC support consistent security awareness practices by the regular identification of new threats which may inspire the creation of new security awareness videos. Social implications The structure of OSNs is making it easier for malicious users to carry out their activities without the possibility of detection. By building a security awareness programme using the TTAT-MIP model, organisations can proactively manage security awareness. Originality/value Many security systems are cumbersome, inconsistent and non-specific. The outcome of this research provides organisations and security practitioners with a framework for designing and developing proactive and tailored security awareness systems.

Predicting Self‐declared Movie Watching Behavior Using Facebook Data and Information‐Fusion Sensitivity Analysis

ABSTRACTThe main purpose of this paper is to evaluate the feasibility of predicting whether yes or no a Facebook user has self‐reported to have watched a given movie genre. Therefore, we apply a data analytical framework that (1) builds and evaluates several predictive models explaining self‐declared movie watching behavior, and (2) provides insight into the importance of the predictors and their relationship with self‐reported movie watching behavior. For the first outcome, we benchmark several algorithms (logistic regression, random forest, adaptive boosting, rotation forest, and naive Bayes) and evaluate their performance using the area under the receiver operating characteristic curve. For the second outcome, we evaluate variable importance and build partial dependence plots using information‐fusion sensitivity analysis for different movie genres. To gather the data, we developed a custom native Facebook app. We resampled our dataset to make it representative of the general Facebook population with respect to age and gender. The results indicate that adaptive boosting outperforms all other algorithms. Time‐ and frequency‐based variables related to media (movies, videos, and music) consumption constitute the list of top variables. To the best of our knowledge, this study is the first to fit predictive models of self‐reported movie watching behavior and provide insights into the relationships that govern these models. Our models can be used as a decision tool for movie producers to target potential movie‐watchers and market their movies more efficiently.

Modeling Spread of Preferences in Social Networks for Sampling-Based Preference Aggregation

Given a large population, it is an intensive task to gather individual preferences over a set of alternatives and arrive at an aggregate or collective preference of the population. We show that social network underlying the population can be harnessed to accomplish this task effectively, by sampling preferences of a small subset of representative nodes. We first develop a Facebook app to create a dataset consisting of preferences of nodes and the underlying social network, using which, we develop models that capture how preferences are distributed among nodes in a typical social network. We hence propose an appropriate objective function for the problem of selecting best representative nodes. We devise two algorithms, namely, Greedy-min which provides a performance guarantee for a wide class of popular voting rules, and Greedy-sum which exhibits excellent performance in practice. We compare the performance of these proposed algorithms against random-polling and popular centrality measures, and provide a detailed analysis of the obtained results. Our analysis suggests that selecting representatives using social network information is advantageous for aggregating preferences related to personal topics (e.g., lifestyle), while random polling with a reasonable sample size is good enough for aggregating preferences related to social topics (e.g., government policies).

Social Smartphone Apps Do Not Capture Attention Despite Their Perceived High Reward Value

Smartphones have been shown to distract people from their main tasks (e.g., studying, working), but the psychological mechanisms underlying these distractions are not clear yet. In a preregistered experiment (https://osf.io/g8kbu/), we tested whether the distracting nature of smartphones stems from their high associated (social) reward value. Participants (N = 117) performed a visual search task while they were distracted by (a) high social reward apps (e.g., Facebook app icon + notification sign), (b) low social reward apps (e.g., Facebook app icon), and (c) no social reward apps (e.g., Weather app icon). We expected that high social reward app icons would slow down search, especially when people were deprived of their smartphones. Surprisingly, high social reward (vs. low or no social reward) apps did not impair visual search performance, yet in a survey (N = 158) participants indicated to perceive these icons as more rewarding. Our results demonstrate that even if people perceive social smartphone apps as more rewarding than nonsocial apps, this may not manifest in behavior.