Facebook User Data Research Articles

To scrape or not to scrape, this is dilemma. The post-API scenario and implications on digital research.

This article aims to investigate the potential impact of restricted social data access on digital research practices. The 2018 Cambridge Analytica scandal exposed the exploitation of Facebook user data for speculative purposes and led to the end of the so-called "Data Golden Age," characterized by free access to social media user data. As a result, many social platforms have limited or entirely banned data access. This policy shift, referred to as the "APIcalypse," has revolutionized digital research methods. To address the impact of this policy shift on digital research, a non-probabilistic sample of Italian researchers was surveyed and the responses were analyzed. The survey was designed to explore how constraints on digital data access have altered research practices, whether we are truly in a post-API era with a radical change in data scraping strategies, and what shared and sustainable solutions can be identified for the post-API scenario. The findings highlight how limits on social data access have not yet created a "post-Api" scenario as expected, but it is turning research practices upside down, positively and negatively. On the positive side, because researchers are experimenting with innovative forms of scraping. Negatively, because there could be a "mass migration" to the few platforms that freely grant their APIs, with critical consequences for the quality of research. The closure of many social media APIs has not opened up a post-API world, but has worsened the conditions of making research, which is increasingly oriented to "easy-data" environments such as Twitter. This should prompt digital researchers to make a self-reflexive effort to diversify research platforms and especially to act ethically with user data. It would also be important for the scientific world and large platforms to enter into understandings for open and conscious sharing of data in the name of scientific progress.

Individual users' participation on political Facebook pages

Social media platforms such as Facebook enable citizens to participate in politics by engaging with content from parties and politicians. Most research has described these activites by means of survey self-reports, smaller sample studies which combined surveys and digital trace data, or larger-scale aggregate digital trace data. The current literature lacks a large-scale descriptive account of individual users' interactions with political content. We analyze a large-scale collection of individual-level Facebook user data from the German federal election year 2017. The data contain millions of interactions by over 2.5 million unique users on 320 Facebook pages of major parties in Germany. They include almost all possible ways to publicly interact with content on these pages and as such cannot be collected today due to newer access restrictions. A large share of users participated only once, especially on the top politicians' pages, or interacted only with a single page. However, we also found a sizeable group of users who were active on many different pages even across party boundaries, and that these users were responsible for a majority of comments and reactions on almost all pages. In addition, there were substantial differences in user participation on the main national party pages and the ones of top politicians on the one hand, and the less prominent pages on the other hand. Our large-scale quantitative description provides context for previous and future smaller-scale in-depth analyses.

ANALISIS SENTIMEN TWEET KASUS KEBOCORAN DATA PENGGUNAAN FACEBOOK OLEH CAMBRIGDE ANALYTICA

The case of the Facebook user data leak by Cambridge Analytica has been spotlight in the public lately. Many of the citizens has participated discussing this case, especially in social media Twitter. Sentiment analysis is a computational research of opinions and emotions sentiment that are expressed textually. This study aims to classify positive and negative sentiment from Twitter data and to determine the accuracy of the classification model using Naïve Bayes Classifier method. Based on experiment conducted by tweet data with the “Zuckerberg” and “Cambridge Analytics” keywords, it has been produced Naïve Bayes Classifier with an accuracy of 83.06%.

Misuse of Facebook user data

Nowadays, we live when technological developments have developed rapidly, especially in the communications and information sector. One of the triggers is social media, including Facebook. In 2010, there were around 24 million Facebook users in Indonesia. In building social relationships, of course, personal information is needed from someone. The same is the case when using social media. When you want to create a Facebook account, users will be asked to provide various personal data. This functions so that users can recognize one another. Besides that, it can also help Facebook in securing user accounts. But by sharing personal data will undoubtedly pose a risk of being misused. Apart from data leaks due to Facebook's shortcomings, this is also inseparable from users' negligence in safeguarding their data. This research explores the unawareness of Facebook users in protecting their data. This research will show the consequences of a lack of attention in maintaining personal data and show how to protect personal data to increase the awareness of Facebook users in safeguarding their data.

People-centric collective intelligence: decentralized and enhanced privacy mobile crowd sensing based on blockchain

Smart phones have changed the way we interact with this world and our peers. They have not only become powerful, but they have become affordable. In this current day and age, smart phones are getting smarter with every subsequent iteration. Every aspect of today’s smart phones is far more efficient and powerful than the computers we had a few years back. Mobile crowd sensing (MCS) is the recent attractive research area that follows the crowd’s basic principle wisdom. Complex tasks can be performed easily with (MCS) network using the enormous amount of human involvement and powerful mobile devices. Even though there are a plethora of options available to users in terms of services to choose from, the one thing that remains a concern is security, privacy & data breach. Mobile crowd sensing -centralized design approach can give a lot of durable services. However, the trust of centralized service providers cannot be assured within the real world, owing to the profit-driven facts of service providers, when the privacy policy goes into conflict with their profit, they’re most likely to perform dishonest work, going against the work ethics and even performing malicious activities for gaining more profit. The prime example is Mark Zuckerberg providing their Facebook User data to third parties for monetary benefits. Besides the emergence of assorted attacks, particularly for inner attacks, service providers cannot offer full protection for task information, attribution information, or collected knowledge. Therefore, a decentralized design approach for mobile crowd sensing is strongly required. This paper aims to design and implement a real-time privacy-preserving data aggregation distributed-scheme to mobile crowd sensing called SMARTEE. Our design uses elliptic curve-based Public Key Encryption and digital signature for data encryption and verification, which performs faster computations, uses less storage and employs a shorter key. The digital signature ensures integrity and privacy-preserving of the data. We incorporate blockchain technology with the traditional (MCS) to process and analyze obtained crowd sensed data that ensures automotive security and privacy. Our proposed architecture with the features of blockchain protects the user information's privacy and increases the security of the (MCS) environment.

Detecting fake news on Facebook: The role of emotional intelligence.

The proliferation of fake news on social media is now a matter of considerable public and governmental concern. In 2016, the UK EU referendum and the US Presidential election were both marked by social media misinformation campaigns, which have subsequently reduced trust in democratic processes. More recently, during the COVID-19 pandemic, the acceptance of fake news has been shown to pose a threat to public health. Research on how to combat the false acceptance of fake news is still in its infancy. However, recent studies have started to focus on the psychological factors which might make some individuals less likely to fall for fake news. Here, we adopt that approach to assess whether individuals who show high levels of ‘emotional intelligence’ (EQ) are less likely to fall for fake news items. That is, are individuals who are better able to disregard the emotionally charged content of such items, better equipped to assess the veracity of the information. Using a sample of UK participants, an established measure of EQ and a novel fake news detection task, we report a significant positive relationship between individual differences in emotional intelligence and fake news detection ability. We also report a similar effect for higher levels of educational attainment, and we report some exploratory qualitative fake news judgement data. Our findings are discussed in terms of their applicability to practical short term (i.e. current Facebook user data) and medium term (i.e. emotional intelligence training) interventions which could enhance fake news detection.

Rumor has it: The role of social ties and misinformation in evacuation to nearby shelters after disaster

When crisis strikes, why do some communities utilize evacuation shelters more than others? This mixed methods study draws on a new dataset of almost-daily tallies of evacuees at 660 local shelters following Japan’s 2018 Eastern Iburi Earthquake in Hokkaido to create a large-N time-series cross sectional (TSCS) dataset of local, short-distance evacuation. We pair time-series cross-sectional data models with qualitative comparative analysis (QCA) of nine affected municipalities to examine why some shelters see higher evacuation rates than others. While past studies have used Facebook user data, post-hoc surveys, or ad-hoc roadside interviews to measure evacuation, this study uses meticulously recorded shelter attendance data to draw inferences about evacuation behavior. Controlling for types of shelters, damage levels, infrastructure quality, social vulnerability, governance capacity, and community resources, we find that in affected communities, stronger bridging social ties, especially when aided by linking ties, motivate greater evacuation to shelters. In unaffected communities, stronger bonding and bridging ties encourage potentially unnecessary evacuation, helping spread rumors during blackouts. These results highlight the necessity of clear, transparent communication with the public, and fostering trust in government during crises.

Cambridge Analytica’s black box

The Cambridge Analytica–Facebook scandal led to widespread concern over the methods deployed by Cambridge Analytica to target voters through psychographic profiling algorithms, built upon Facebook user data. The scandal ultimately led to a record-breaking $5 billion penalty imposed upon Facebook by the Federal Trade Commission (FTC) in July 2019. The FTC action, however, has been criticized as failing to adequately address the privacy and other harms emanating from Facebook’s release of approximately 87 million Facebook users’ data, which was exploited without user authorization. This Essay summarizes the FTC’s response to the Cambridge Analytica–Facebook scandal. It concludes that the scandal focuses attention on the need to explore the potential for embedding due process-type inquiries and protections within the enforcement actions by regulatory agencies such as the FTC. These protections are increasingly important in addressing the problem of “black boxing the voter” that is now presented by data- and algorithmic-driven companies such as Cambridge Analytica and Facebook.

Facebook Recruitment Using Zip Codes to Improve Diversity in Health Research: Longitudinal Observational Study.

BackgroundFacebook’s advertising platform reaches most US households and has been used for health-related research recruitment. The platform allows for advertising segmentation by age, gender, and location; however, it does not explicitly allow for targeting by race or ethnicity to facilitate a diverse participant pool.ObjectiveThis study looked at the efficacy of zip code targeting in Facebook advertising to reach blacks/African Americans and Hispanics/Latinos who smoke daily for a quit-smoking web-based social media study.MethodsWe ran a general market campaign for 61 weeks using all continental US zip codes as a baseline. Concurrently, we ran 2 campaigns to reach black/African American and Hispanic-/Latino-identified adults, targeting zip codes ranked first by the percentage of households of the racial or ethnic group of interest and then by cigarette expenditure per household. We also ran a Spanish language campaign for 13 weeks, targeting all continental US zip codes but utilizing Facebook’s Spanish language targeting. The advertising images and language were common across campaigns. Costs were compared for advertisement clicks, queries, applications, and participants, and yields were compared for the final three outcomes. We examined outcomes before and after the Cambridge Analytica scandal that broke in March 2018. Finally, we examined 2 promoted Facebook features: lookalike audiences and audience network placement.ResultsZip code targeting campaigns were effective for yielding the racial or ethnic groups of interest. The black-/African American–focused versus general market campaign increased black/African American weekly queries (mean 9.48, SD 5.69 vs general market mean 2.83, SD 2.05; P<.001) and applicants (mean 1.11, SD 1.21 vs general market mean 0.54, SD 0.58; P<.001). The Hispanic-/Latino-focused versus general market campaign increased Hispanic/Latino weekly queries (mean 3.10, SD 2.16 vs general market mean 0.71, SD 0.48; P<.001) and applicants (mean 0.36, SD 0.55 vs general market mean 0.10, SD 0.14; P=.001). Cost metrics did not differ between campaigns at generating participants (overall P=.54). Costs increased post- versus prescandal for the black-/African American–focused campaign for queries (mean US $8.51, SD 3.08 vs US $5.87, SD 1.89; P=.001) and applicants (mean US $59.64, SD 35.63 vs US $38.96, SD 28.31; P=.004) and for the Hispanic-/Latino-focused campaign for queries (mean US $9.24, SD 4.74 vs US $7.04, SD 3.39; P=.005) and applicants (mean US $61.19, SD 40.08 vs US $38.19, SD 21.20; P=.001).ConclusionsZip code targeting in Facebook advertising is an effective way to recruit diverse populations for health-based interventions. Audience network placement should be avoided. The Facebook lookalike audience may not be necessary for recruitment, with drawbacks including an unknown algorithm and unclear use of Facebook user data, and so public concerns around data privacy should be considered.Trial RegistrationClinicalTrial.gov NCT02823028; https://clinicaltrials.gov/ct2/show/NCT02823028

Rumor Has it: The Role of Social Ties in Evacuation to Nearby Shelters after the 2018 Hokkaido Earthquake

When crisis strikes, why do some communities utilize evacuation shelters more than others? This mixed methods study draws on a new dataset of almost-daily tallies of evacuees at 660 local shelters following Japan’s 2018 Eastern Iburi Earthquake in Hokkaido to create a large-N time-series cross sectional (TSCS) dataset of local, short-distance evacuation. We pair time-series cross-sectional data models with qualitative comparative analysis (QCA) of nine affected municipalities to examine why some shelters see higher evacuation rates than others. While past studies have used Facebook user data, post-hoc surveys, or ad-hoc roadside interviews to measure evacuation, this study uses meticulously recorded shelter attendance data to draw inferences about evacuation behavior. Controlling for types of shelters, damage levels, infrastructure quality, social vulnerability, governance capacity, and community resources, we find that in affected communities, stronger bridging social ties, especially when aided by linking ties, motivate greater evacuation to shelters. In unaffected communities, stronger bonding and bridging ties encourage potentially unnecessary evacuation, helping spread rumors during blackouts. These results highlight the necessity of clear, transparent communication with the public, and fostering trust in government during crises.

Brief of the Center for the Legalization of Privacy as Amicus Curiae in Opposition to Consent Motion, United States v. Facebook

In this brief, I argue three things with respect to the pending Consent Motion in United States v. Facebook: 1. The Stipulated Order, as Written, Can be Reasonably Interpreted to Grant the Federal Trade Commission and the Department of Justice Warrantless Access to Facebook User Data. 2. The Stipulated Order, Insofar as it Permits Warrantless Access to Facebook User Data, Rests on an Unjustified Assumption about the Validity and Scope of the “Third-Party Doctrine.” 3. The Assumption Made in the Stipulated Order is Particularly Unjustified in Light of Carpenter v. United States.

Behavioral Intention of Using Social Networking Site

Although major contributions are being made by Social Networking Sites (SNS) in communications and e-commerce, very little attention has been given to its implementation. This study contributes to technology and human interaction domain by (1) extending the technology acceptance model (TAM), personality trait and telepresence in developing a comparative mode based on the behavioral intention, (2) testing the model with Partial Least Square (PLS) using Facebook user's data; and (3) comparing result across a developed (Taiwan) and an emerging (Thailand) economy. Results indicate TAM was to fit to test the behavioral intention model in these two economies. The findings support for the telepresence, perceived usefulness, and perceived ease of use, attitude and behavioral intention; however, the effect levels are different in these two countries (Taiwan vs. Thailand). In addition, only extraversion, conscientiousness, agreeableness, and neuroticism of the big-five personality have been discussed.

Design and Effects of Personal Visualizations

In an effort to determine what elements need to be considered when designing personal visualizations, this research study explores how users react to different personal visualization designs. The authors present three distinct personal visualization designs (Timeline, Spark, and Bouquet) for visualizing Facebook user data. In a scenario-based user study, they interview participants to evaluation each design's utility, exploring the visualization of the users' own data, comparing two personal visualizations, and analyzing a series of personal visualizations. The analysis of the recorded conversations suggests that the illustrative design was the most well-balanced with respect to relaying information, motivating data exploration, and providing personal insights to the users. The authors believe this study will help guide future developers regarding the design of personal visualizations.

FACEBOOK ADVERTISEMENTS FOR SURVEY PARTICIPANT RECRUITMENT: CONSIDERATIONS FROM A MULTI-NATIONAL STUDY

Facebook's global reach suggests good potential for recruiting research participants and collecting objective behavioral data for cross-cultural research. Previous literature suggests the usefulness of Facebook advertisements to recruit participants in single-country studies. However, Facebook advert use in multi-country studies has not yet been reported. Nor are there any reports about soliciting Facebook user data (via Facebook applications) using Facebook advertisements. This paper contributes to this gap in Internet research, reporting on the effectiveness of Facebook advertisements to recruit participants, and for soliciting anonymized Facebook user data, in a 20-country study about privacy concerns on Facebook. Over seven days, 399 Facebook users from 18 countries responded to country-targeted advertisements in 13 languages. Response rates (ratio of advert clicks to valid responses) per country varied from 0% up to 14%. Overall, two-thirds of the country response rates were below 5%, and many country samples were gender-biased due to confounding societal factors. We conclude that for multi-national studies, Facebook advertisements may have potential for simple participant recruitment for surveys, but have limitations for soliciting Facebook user data. For user data collection, methods such as Amazon Mechanical Turk and snowball sampling may be more effective, but can be limited in their international reach.