This database is comprised of 603 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 208 males (34%) and 395 females (66%). Their ages ranged from 12 to 15 years. Their age in years at baseline is provided. The majority were born in Australia. Data were drawn from students at two Australian independent secondary schools. The data contains total responses for the following scales: The Intolerance of Uncertainty Scale (IUS-12; Short form; Carleton et al, 2007) is a 12-item scale measuring two dimensions of Prospective and Inhibitory intolerance of uncertainty. Two subscales of the Children’s Automatic Thoughts Scale (CATS; Schniering & Rapee, 2002) were administered. The Peronalising and Social Threat were each composed of 10 items. UPPS Impulsive Behaviour Scale (Whiteside & Lynam, 2001) which is comprised of 12 items. Dispositional Envy Scale (DES; Smith et al, 1999) which is comprised of 8 items. Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. Three subscales totals included were the GAD subscale (labelled SCAS_GAD), the OCD subscale (labelled SCAS_OCD) and the Social Anxiety subscale (labelled SCAS_SA). Each subscale was comprised of 6 items. Avoidance and Fusion Questionnaire for Youth (AFQ-Y; Greco et al., 2008) which is comprised of 17 items. Distress Disclosure Index (DDI; Kahn & Hessling, 2001) which is comprised of 12 items. Repetitive Thinking Questionnaire-10 (RTQ-10; McEvoy et al., 2014) which is comprised of 10 items. The Brief Fear of Negative Evaluation Scale, Straightforward Items (BFNE-S; Rodebaugh et al., 2004) which is comprised of 8 items. Short Mood and Feelings Questionnaire (SMFQ; Angold et al., 1995) which is comprised by 13 items. The Self-Compassion Scale Short Form (SCS-SF; Raes et al., 2011) which is comprised by 12 items. The subscales include Self Kindness, Self Judgment, Social Media subscales - These subscale scores were based on social media questions composed for this project and also drawn from three separate scales as indicated in the table below. The original scales assessed whether participants experience discomfort and a fear of missing out when disconnected from social media (taken from the Australian Psychological Society Stress and Wellbeing Survey; Australian Psychological Society, 2015a), style of social media use (Tandoc et al., 2015b) and Fear of Missing Out (Przybylski et al., 2013c). The items in each subscale are listed below. Pub_Share Public Sharing When I have a good time it is important for me to share the details onlinec On social media how often do you write a status updateb On social media how often do you post photosb Surveillance_SM On social media how often do you read the newsfeed On social media how often do you read a friend’s status updateb On social media how often do you view a friend’s photob On social media how often do you browse a friend’s timelineb Upset Share On social media how often do you go online to share things that have upset you? Text private On social media how often do you Text friends privately to share things that have upset you? Insight_SM Social Media Reduction I use social media less now because it often made me feel inadequate FOMO I am afraid that I will miss out on something if I don’t stay connected to my online social networksa. I feel worried and uncomfortable when I can’t access my social media accountsa. Neg Eff of SM I find it difficult to relax or sleep after spending time on social networking sitesa. I feel my brain ‘burnout’ with the constant connectivity of social mediaa. I notice I feel envy when I use social media.
I can easily detach from the envy that appears following the use of social media (reverse scored) DES_SM Envy Mean acts online Feeling envious about another person has led me to post a comment online about another person to make them laugh Feeling envious has led me to post a photo online without someone’s permission to make them angry or to make fun of them Feeling envious has prompted me to keep another student out of things on purpose, excluding her from my group of friends or ignoring them. Substance Use: Two items measuring peer influence on alcohol consumption were adapted from the SHAHRP “Patterns of Alcohol Use” measure (McBride, Farringdon & Midford, 2000). These items were “When I am with friends I am quite likely to drink too much alcohol” and “Substances (alcohol, drugs, medication) are the immediate way I respond to my thoughts about a situation when I feel distressed or upset. Angold, A., Costello, E. J., Messer, S. C., & Pickles, A. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5(4), 237–249. Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Greco, L.A., Lambert, W. & Baer., R.A. (2008) Psychological inflexibility in childhood and adolescence: Development and evaluation of the Avoidance and Fusion Questionnaire for Youth. Psychological Assessment, 20, 93-102. https://doi.org/10.1037/1040-3590.20.2.9 Kahn, J. H., & Hessling, R. M. (2001). Measuring the tendency to conceal versus disclose psychological distress. Journal of Social and Clinical Psychology, 20(1), 41–65. https://doi.org/10.1521/jscp.20.1.41.22254 McBride, N., Farringdon, F. & Midford, R. (2000) What harms do young Australians experience in alcohol use situations. Australian and New Zealand Journal of Public Health, 24, 54–60 https://doi.org/10.1111/j.1467-842x.2000.tb00723.x McEvoy, P.M., Thibodeau, M.A., Asmundson, G.J.G. (2014) Trait Repetitive Negative Thinking: A brief transdiagnostic assessment. Journal of Experimental Psychopathology, 5, 1-17. Doi. 10.5127/jep.037813 Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in human behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Rodebaugh, T. L., Woods, C. M., Thissen, D. M., Heimberg, R. G., Chambless, D. L., & Rapee, R. M. (2004). More information from fewer questions: the factor structure and item properties of the original and brief fear of negative evaluation scale. Psychological assessment, 16(2), 169. https://doi.org/10.1037/10403590.16.2.169 Schniering, C. A., & Rapee, R. M. (2002). Development and validation of a measure of children’s automatic thoughts: the children’s automatic thoughts scale. Behaviour Research and Therapy, 40(9), 1091-1109. . https://doi.org/10.1016/S0005-7967(02)00022-0 Smith, R. H., Parrott, W. G., Diener, E. F., Hoyle, R. H., & Kim, S. H. (1999). Dispositional envy. Personality and Social Psychology Bulletin, 25(8), 1007-1020. https://doi.org/10.1177/01461672992511008 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5 Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053 Whiteside, S.P. & Lynam, D.R. (2001) The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Personality and Individual Differences 30,669-689. https://doi.org/10.1016/S0191-8869(00)00064-7 The data was collected by Dr Danielle A Einstein, Dr Madeleine Fraser, Dr Anne McMaugh, Prof Peter McEvoy, Prof Ron Rapee, Assoc/Prof Maree Abbott, Prof Warren Mansell and Dr Eyal Karin as part of the Insights Project. The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

According to a 2023 survey conducted in the United States, teenagers spent an average of 4.8 hours every day on social media platforms. Girls spent 5.3 hours on social networks daily, compared to 4.4 hours for boys. YouTube and TikTok were the most popular online networks among those aged 13 to 19, with 1.9 and 1.5 hours of average daily engagement, respectively. The most used platform for girls was TikTok, while the most used platform for boys was YouTube. Are teens constantly connected to social media? YouTube, TikTok, Instagram and Snapchat are the most attractive and time-consuming platforms for young internet users. A survey conducted in the U.S. in 2023 found that 62 percent of teenagers were almost constantly connected to Instagram, and 17 percent were almost constantly connected to TikTok. Overall, 71 percent of teens used YouTube daily, and 47 percent used Snapchat daily. Furthermore, YouTube had a 93 percent reach among American teens in 2023, down from 95 percent in 2022. Teens and their internet devices For younger generations especially, social media is mostly accessed via mobile devices, and almost all teenagers in the United States have smartphone access. A 2023 survey conducted in the U.S. found that 92 percent of teens aged 13 to 14 years had access to a smartphone at home, as well as 97 percent of those aged 15 to 17. Additionally, U.S. girls were slightly more likely than their male counterparts to have access to a smartphone.

Compulsive buying refers to a consumer’s tendency to be preoccupied with buying which is revealed through repetitive buying and lack of impulse control (Ridgway et al., 2008). Although it has yet to be formally recognized as an independent mental disorder in either the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) or the International Classification of Diseases, Eleventh Revision (ICD-11), research by Müller et al. (2021) has proposed partial diagnostic criteria. These include persistent or recurrent dysfunctional buying behaviors that persist despite adverse consequences, a pattern that does not emerge solely during manic or hypomanic episodes, and purchases that cannot be ascribed to other medical conditions. Challet-Bouju et al. (2020) proposes that compulsive buying has similar psychological characteristics to other addictive behaviors, and it can be considered to supplement compulsive buying as a class of addiction disorders. Previous studies have demonstrated that compulsive buying can lead to numerous adverse effects, including loan dependency, a decrement in quality of life, and interpersonal conflicts (Owusu et al., 2021; Zhang et al., 2017; McElroy et al., 1994). To effectively address and intervene in compulsive buying, it is crucial to explore its underlying influencing factors. Research has unearthed a plethora of elements that might contribute to this behavior, such as consumers' personality traits (Tarka et al., 2021), depression (Çelik & Köse, 2021), and psychoticism (Harnish et al., 2021). The prevalence of compulsive buying among college students is particularly worrisome. Estimates suggest that the overall rate of compulsive online buying among college students reaches 8.4%, and such behavior can trigger an upsurge in negative emotions like regret and depression (Zeng & Chen, 2015). Meanwhile, another study indicated that the incidence of compulsive purchases among college students stands at approximately 8.3%, significantly higher than the 4.9% observed among adults (Maraz et al., 2015). Self-identity may be an important factor influencing compulsive buying (Moulding, Kings, & Knight, 2021). According to Erikson (1959), self-identity represents an individual's subjective perception of who they were, are, and will be, constituting the explicit and implicit responses to the fundamental question of "Who am I?". People with a high level of self-identity have a clearer sense of themselves. Marcia (1966) manipulates Erikson's view of self-identity and then developed a theoretical model of self-identity status, known as the Identity Status Theory. According to this theory, based on two dimensions - "crisis" and "commitment", the development of self-identity can be divided into four main statuses. “Identity achievement” means individuals have gone through the crisis stage of exploring various options and made firm commitments to specific goals, values and beliefs. They know clearly who they are and where they are heading. “Identity moratorium” indicates that individuals are in the middle of a crisis, actively exploring different possibilities without making a definite commitment yet. “Identity foreclosure” occurs when people make premature commitments without experiencing a crisis, usually adopting the goals and values set by parents or others. “Identity diffusion” refers to those who neither experience a crisis nor make commitments, lacking a clear understanding of their own identity and having no definite plans for the future. Rousseau (1998) proposed that self-identity is how an individual perceives and internalizes his or her role. Cognitive dissonance theory posits that individuals feel psychological tension when there's a conflict between their beliefs, attitudes, or behaviors, and may alter them to reduce this tension. When self-identity is low, people might resolve the cognitive dissonance between their desired and actual selves through compulsive buying. For instance, if someone wants to be trendy but isn't, they may buy items to seem more successful or popular. The symbolic self-completion theory suggests that individuals try to achieve self-identity by bridging the gap to their ideal states via specific behaviors. Those with low self-identity might use the symbolic meanings of purchased items to shape their ideal self-image. A college student unsure about their career, seeing businesspeople with high-end electronics in ads, might buy similar products to appear more professional, potentially leading to compulsive buying. Dittmar et al. (1996) argues that if individuals use consumption as a self-fulfilling strategy, the degree of self-differences can predict impulse purchase frequency. A study found that people with higher self-identity will have stronger self-control abilities, and thus more control over buying behavior (Jiang et al. 2022). Experimental studies have also shown that exploring self-identity may promote spending self-control by strengthening goal-setting capacities (Vosylis et al., 2019), thus may reducing compulsive buying. Based on these previous studies, we thought self-identity might be linked to compulsive buying. People with low self-identity may have higher levels of compulsive buying. The self-identity of college students remains in a developmental stage. A survey in Guangdong Province, China, found that only 0.5% of college students was in a state of identity completion, which refer to a stage where individuals have achieved a clear, stable, and coherent sense of self, including their personal values, beliefs, and life goals (Hou & Yang, 2021). This also makes it particularly important to take self-identity into account in research with college students. While Hou & Yang (2021) suggest a linear correlation between self-identity and online shopping addiction, their findings are limited to the online shopping context. Therefore, we aimed to make exploration into the relationship between self-identity and compulsive buying behaviors in a more general sense which is not only the online context but also the offline context. Besides, we considered passive social media use as a mediating variable. The general increase in the use of various social media due to the COVID-19 outbreak and the quarantine measures makes it an extraordinarily interesting mediator (Tuck & Thompson., 2021). Passive social network use refers to browsing the content on the social network without communicating with others, such as viewing their status update (Burke et al., 2010). Social learning theory posits that individuals learn behaviors, including consumption behaviors, through observation, imitation, and reinforcement. In the context of compulsive buying, college students may observe their peers' buying behaviors on social media or in real-life settings. If they see others getting positive comments on social media, they are more likely to want to mimic such behavior. For example, when they see that other people's photography is appreciated, they may have a desire to buy a camera. Studies suggested that the use of passive social networks may improve upward social comparison and thus produce compulsive buying behavior (Zheng et al., 2020). Also, Bloggers on social networks influence their potential users and promote related products, triggering more purchasing behaviors (Sokolova & Kéfi, 2020). Sharif et al. (2021) suggested that heavy social networking may lead to financial social comparison and increased materialism, which in turn drive the tendency for online compulsive buying. In addition, the research among teenagers further confirms that lower levels of self-identity and psychological harmony are associated with higher levels of overuse of social networks (Jiang et al., 2017). It is reasonable to speculate that the simple passive social networking use of just browsing without engaging may also link self-identity and compulsive buying. But so far, no research has directly explored the impact of passive social media use on the relationship between these two factors. We hypothesize that individuals with lower self-identity are more likely to engage in passive social network use, resulting in compulsive purchasing behavior. Studies have shown that self-esteem is associated with compulsive buying respectively (O’Guinn & Faber, 1989; Roberts et al., 2014; Yurchisin & Johnson, 2004). Biolcati (2017) pointed that low self-esteem can be a strong predictor of compulsive buying, and fear of negative evaluation plays a mediating role between self-esteem and compulsive buying behavior only for women. We assumed that high self-esteem enhances the inhibitory effect of self-identity on compulsive buying. Individuals with high self-esteem are more confident in their decisions and more able to resist impulse spending. On the contrary, low self-esteem will weaken the inhibitory effect of self-identity on compulsive buying. Therefore, we suggest that self-esteem may be used as a moderating variable to mediate the effects of self-identity on compulsive shopping. Similarly, we suggest that low self-esteem enhances the promoting effect of passive social network use on compulsive shopping. At the same time, studies have shown that low self-esteem is associated with overuse of social networks (Kim et al., 2021), so we speculate that low self-esteem may weaken the inhibitory effect of high self-identity on passive social network use. Therefore, it is necessary to investigate the role that self-esteem specifically plays in the relationship between self-identity and compulsive buying. In summary, the present study proposed a moderated mediation model to explore the internal psychological mechanisms underlying the effect of self-identity on compulsive buying. The study was guided by the following hypotheses: H1. There is an association between self-identity and compulsive buying. H2. Passive use of social network sites will mediate the relationship between

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

The data in this collection are social network data drawn from a large-scale field experiment. Theories of human behavior suggest that individuals attend to the behavior of certain people in their community to understand what is socially normative and adjust their own behavior in response. This experiment tested these theories by randomizing an anti-conflict intervention across 56 New Jersey public middle schools, with 24,191 students. After having comprehensively measured every school's social network, randomly selected seed groups of 20-32 students from randomly selected schools were assigned to an intervention that encouraged public stances against conflict at school. The data allowed for comparisons between treatment and control groups, and also provided variables to analyze social networks to examine the impact of social referents. Surveys were conducted at the start and end of the 2012-2013 school year, the year in which the experiment was conducted. The survey data contains social network variables based on the peers with whom the respondent chooses to spend time. Survey data also include respondents' perceived descriptive and prescriptive norms of conflict at the schools surveyed, as well as administrative data on the schools and demographics of respondents. The collection includes one dataset, with 482 variables for 24,471 cases. Demographic variables in the collection include gender, grade, age, height, weight, race/ethnicity, language, household characteristics, and demographic variables obtained from school administrative records.

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.

The C-19GA20 dataset was gathered online in April 2020 from school and university students between 14 to 24 years of age. It provides insightful information about the students’ mental health, social lives, attitude towards Covid-19, impact of the Covid-19 Pandemic on students’ education, and their experience with online learning. The data includes 5 major groups of variables: 1) Socio-demographic data - age group, gender, current place of stay, study level in their institution 2) 4 items for information regarding connectivity to the internet during the lockdown - device availability for exclusive use, internet bandwidth, top 5 online tools used most commonly, and screen time. 3) 9 items measured the impact of Covid-19 on the students’ social lives - their current situation of living, number of people around them where they live, their feelings towards meeting their friends, visiting their institution of study, events that would have been held offline. Students were asked about their top 5 past time activities during the lockdown and the amount of time they spend on social media online. 4) 6 items to gauge their experience with online learning during the lockdown - questions about feeling connected to their peers, maintaining discipline, structured learning, and the stress/burden felt by them due to online learning in the lockdown 5) 11 items to comprehensively gather information about the students’ mental health - how well have they adapted to stay-at-home instructions, their overall mood in the lockdown, feelings towards Covid 19, their prime concerns regarding their academic schedule, being updated and informed about Covid 19, the impact of social media on their beliefs. Finally, the students were asked to write about how they feel the pandemic has changed them as a person and affected their thinking process, and the students were asked to share a one-line message for the world during the lockdown.

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

As of January 2024, #love was the most used hashtag on Instagram, being included in over two billion posts on the social media platform. #Instagood and #instagram were used over one billion times as of early 2024.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

The United Negro College Fund (UNCF)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Background: Facebook addiction is said to occur when an individual spends an excessive amount of time on Facebook, disrupting one’s daily activities and social life. The present study aimed to find out the level of Facebook addiction in the Nepalese context and briefly discuss the crimes associated with its unintended use. Methods: A descriptive cross-sectional study was conducted in the Department of Forensic Medicine of Lumbini Medical College. The study instrument was the Bergen Facebook Addiction Scale typed into a Google Form and sent randomly to Facebook contacts of the authors. The responses were downloaded in a Microsoft Excel spreadsheet and analyzed using Statistical Package for Social Sciences version 16. Results: The study consisted of 103 Nepalese participants, of which 54 (52.42%) were males and 49 females (47.58%). There were 11 participants (10.68%) who had more than one Facebook account. When different approaches were applied it was observed that 8.73% (n=9) to 39.80% (...

Instagram’s most popular post

              As of April 2024, the most popular post on Instagram was Lionel Messi and his teammates after winning the 2022 FIFA World Cup with Argentina, posted by the account @leomessi. Messi's post, which racked up over 61 million likes within a day, knocked off the reigning post, which was 'Photo of an Egg'. Originally posted in January 2021, 'Photo of an Egg' surpassed the world’s most popular Instagram post at that time, which was a photo by Kylie Jenner’s daughter totaling 18 million likes.
              After several cryptic posts published by the account, World Record Egg revealed itself to be a part of a mental health campaign aimed at the pressures of social media use.

              Instagram’s most popular accounts

              As of April 2024, the official Instagram account @instagram had the most followers of any account on the platform, with 672 million followers. Portuguese footballer Cristiano Ronaldo (@cristiano) was the most followed individual with 628 million followers, while Selena Gomez (@selenagomez) was the most followed woman on the platform with 429 million. Additionally, Inter Miami CF striker Lionel Messi (@leomessi) had a total of 502 million. Celebrities such as The Rock, Kylie Jenner, and Ariana Grande all had over 380 million followers each.

              Instagram influencers

              In the United States, the leading content category of Instagram influencers was lifestyle, with 15.25 percent of influencers creating lifestyle content in 2021. Music ranked in second place with 10.96 percent, followed by family with 8.24 percent. Having a large audience can be very lucrative: Instagram influencers in the United States, Canada and the United Kingdom with over 90,000 followers made around 1,221 US dollars per post.

              Instagram around the globe

              Instagram’s worldwide popularity continues to grow, and India is the leading country in terms of number of users, with over 362.9 million users as of January 2024. The United States had 169.65 million Instagram users and Brazil had 134.6 million users. The social media platform was also very popular in Indonesia and Turkey, with 100.9 and 57.1, respectively. As of January 2024, Instagram was the fourth most popular social network in the world, behind Facebook, YouTube and WhatsApp.

As of April 2024, it was found that men between the ages of 25 and 34 years made up Facebook largest audience, accounting for 18.4 percent of global users. Additionally, Facebook's second largest audience base could be found with men aged 18 to 24 years.

              Facebook connects the world

              Founded in 2004 and going public in 2012, Facebook is one of the biggest internet companies in the world with influence that goes beyond social media. It is widely considered as one of the Big Four tech companies, along with Google, Apple, and Amazon (all together known under the acronym GAFA). Facebook is the most popular social network worldwide and the company also owns three other billion-user properties: mobile messaging apps WhatsApp and Facebook Messenger,
              as well as photo-sharing app Instagram. Facebook usersThe vast majority of Facebook users connect to the social network via mobile devices. This is unsurprising, as Facebook has many users in mobile-first online markets. Currently, India ranks first in terms of Facebook audience size with 378 million users. The United States, Brazil, and Indonesia also all have more than 100 million Facebook users each.

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.

As of April 2024, Bahrain was the country with the highest Instagram audience reach with 95.6 percent. Kazakhstan also had a high Instagram audience penetration rate, with 90.8 percent of the population using the social network. In the United Arab Emirates, Turkey, and Brunei, the photo-sharing platform was used by more than 85 percent of each country's population.