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Description of studies

Of 17077 studies screened, 688 full text studies were assessed, with 126 included (73 in the meta-analysis; fig 2). The final sample included 1431534 adolescents (mean age of 15.0 years). Most included studies were cross-sectional (n=99; 79%) and investigated high income countries (n=113; 90%),73 with 44 studies (35%) investigating US adolescents. Appendix 11 shows the geographical distribution of included study populations. Included and excluded study characteristics are presented in appendix 11 and 12.

PRISMA flow diagram. APA=American Psychological Association.*One study92 was not included in the synthesis without meta-analysis (SWiM) as this resulted in counting of study participants twice; we were able to include estimates from this study in meta-analyses stratified by outcome where this issue did not occur

For 122 included cross-sectional and cohort studies, 57 (47%) of studies were graded high risk of bias, 31 (25%) were moderate, and 34 (28%) were low. Of the four randomised controlled trials included, two were graded with some concerns and two as low risk of bias (appendix 13). Reviewer risk of bias agreement was strong (=0.91).79

Within included studies, many social media exposure measures were reported, with most investigating multiple measures (appendix 14). All were incorporated in our exploration of how social media use is measured, therefore, the number of datapoints reported differs across syntheses.

In total, 253 social media measures were reported: 135 (53%) assessed frequency, 61 (24%) assessed exposure to content displaying health risk behaviour, 45 (18%) assessed time spent, and 12 (5%) other social media activities. Despite our broad definition of social media, most included studies assessed a narrow range of social media categories (or adopted a broad definition). Social networking sites was the most common category investigated (56%; n=141). Of those social media measures investigating a specific platform (n=86), Facebook was most investigated (n=40), followed by Twitter (n=10).

Of those 61 measures assessing exposure to content displaying a health risk behaviour, 36 (59%) assessed marketer generated content, 16 (26%) assessed user generated content, and nine (15%) assessed both types of content. In total, 134 (53%) of the 253 social media measures provided sufficient information to differentiate between use that was active (eg, positing and commenting on posts; n=90) or passive (eg, observing others, content, or watching videos; n=44). Exposure ascertainment primarily used unvalidated adolescent self-report surveys (n=221) with a minority using data-driven codes, validated adolescent self-report questionnaires and/or clinical records (n=32).

Alcohol use was the most extensively studied outcome (appendix 15). For time spent, 15/16 studies (93.8%) reported harmful associations (95% confidence interval 71.7% to 98.9%; n=100354; sign test P<0.001), 16/17 studies (94.1%) for frequency (73.0% to 99.0%; n=390843; sign test P<0.001), and 11/12 studies (91.7%) for exposure to content displaying health risk behaviour (64.6% to 98.5%; n=24247; sign test P=0.006). The category other social media activities was investigated by one study (ie, participants had a Facebook account) that reported a harmful association (95% confidence interval 20.7% to 100%; n=4485; fig 3 for effect direction plot).

Effect direction plot for studies of the association between social media use and adolescent alcohol use, by social media exposure. Arrow size indicates sample size; arrow colour indicates study risk of bias. Sample size is represented by the size of the arrow, measured on a log scale. Outcome measure is number of outcome measures synthesised within each study. Studies organised by risk of bias grade, study design, and year of publication. Repeat cross-sectional studies, multiple study populations from different countries, and age subsets originating from the same study reported as separate studies. ESP=Spain; FIN=Finland; KOR=South Korea; NOS=assessed via adapted Newcastle Ottawa Scale; RCS=repeat cross-sectional study; SM=social media

In meta-analyses, frequent or daily (v infrequent or non-daily) social media use was associated with increased alcohol consumption (odds ratio 1.48 (95% confidence interval 1.35 to 1.62); I2=39.3%; n=383068; fig 4A). In stratified analyses (appendix 16, p162-167), effect sizes were larger for adolescents 16 years or older compared with participants who were younger than 16 years (1.80 (1.46 to 2.22) v 1.34 (1.26 to 1.44); P<0.01 for test of differences). Social networking sites were associated with increased alcohol consumption, while microblogging or media sharing sites had an unclear association (P=0.03).

Forest plots for association between frequency of social media use and A) alcohol use, B) drug use, and C) tobacco use. (A) Binary exposure (frequent or daily v infrequent or non-daily) and binary or continuous alcohol use outcome meta-analysis, with OR used as common metric (N=383068). (B) Binary exposure (frequent/daily v infrequent/non-daily) and binary or continuous drug use outcome meta-analysis, with OR used as common metric (N=117645). (C) Binary exposure (frequent v infrequent) and binary or continuous tobacco use outcome meta-analysis, with OR used as common metric (N=424326). Hard drugs were defined by the cited papers as prescription drugs without a doctors prescription (eg, OxyContin), cocaine crack, methamphetamine, ecstasy, heroin, or opioids. CI=confidence interval; ESP=Spain; FIN=Finland; KOR=South Korea; OR=odds ratio; RoB=Risk of bias; SM=social media; SNS=Social networking sites

Social media use for 2 h or more (v <2 h per day) was associated with increased alcohol consumption (odds ratio 2.12 (95% confidence interval 1.53 to 2.95); I2=82.0%; n=12390), as was exposure (v no exposure) to content displaying health risk behaviours (2.43 (1.25 to 4.71); I2=98.0%; n=14731; appendix 16, p168). Stratified analyses for time spent and exposure to health risk behaviour content generally did not show important differences by age and social media category (appendix 16, p169-171). Associations were slightly stronger for exposure to health risk behaviour content in user generated (3.21 (2.37 to 4.33)) versus marketer generated content (2.35 (1.30 to 4.22); P=0.28; appendix 16, p172). Meta-analyses for frequency of use, time spent on social media, and exposure to content displaying health risk behaviour (assessed on a continuous scale) showed similar findings (appendix 16, p173-174). On stratification (appendix 16, p175-179), for exposure to content displaying health risk behaviour, associations were larger for adolescents 16 years or older versus younger than 16 years (Std.Beta 0.35 (0.29 to 0.42) v 0.09 (0.05 to 0.13); P<0.001). The results indicated that for every one standard deviation increase in exposure to content displaying health risk behaviour, alcohol consumption increased by 0.35 standard deviation for older adolescents compared with 0.09 standard deviation for younger adolescents.

For drug use, across all exposures investigated, 86.6% of studies (n=13/15; 53.3% low/moderate risk of bias) reported harmful associations (appendix 16, p180). The pooled odds ratio for frequent or daily use (v infrequent or non-daily) was 1.28 ((95% confidence interval 1.05 to 1.56), I2=73.2%; n=117645) (fig 4B). Stratification showed no clear differences (appendix 16, p182-184). Few studies (n=3) assessed time spent on social media with estimates suggestive of harm (odds ratio 1.45 (95% confidence interval 0.80 to 2.64); I2=87.4%; n=7357 for 1 h v >1 h/day) (appendix 16, p185).

For tobacco use, 88.9% (n=16/18; 50.0% low risk of bias) studies reported harmful associations of social media use (appendix 16, p 186). Frequent (v infrequent) use was associated with increased tobacco use (odds ratio 1.85 (95% confidence interval 1.49 to 2.30); I2=95.7%; n=424326) (fig 4C), as was exposure (v no exposure) to content displaying health risk behaviours (specifically, marketer generated content) (1.79 (1.63 to 1.96); I2=0.00%; n=22882) (appendix 16, p188). In stratified analyses (appendix 16, p189-193) for frequency of use, stronger associations were observed for low and middle income countries versus for high income countries (2.47 (1.56 to 3.91) v 1.72 (1.35 to 2.19); P=0.17), and for use of social networking sites versus for general social media (2.09 (1.72 to 2.53) v 1.48 (1.01 to 2.18; P=0.29).

Across all exposures investigated, 88.9% of studies (n=8/9; 77.8% low/moderate risk of bias) reported harmful associations on electronic nicotine delivery system use (appendix 16, p194). Exposure to content displaying health risk behaviour (specifically marketer generated content) (v no exposure) was associated with increased electronic nicotine delivery system use (odds ratio 1.73 (95% confidence interval 1.34 to 2.23); I2=63.4%; n=721322) (appendix 16, p195). No clear differences were identified on stratification (appendix 16, p196-197).

After excluding one study with inconsistent findings, across all exposures investigated 90.3% (n=28/31; 67.7% high risk of bias) reported harmful associations for sexual risk behaviours (appendix 16, p 198). Frequent or at all use (v infrequent or not at all) was associated with increased sexual risk behaviours (eg, sending a so-called sext, transactional sex, and inconsistent condom use) (odds ratio 1.77 (95% confidence interval 1.48 to 2.12); I2=78.1%; n=47280) (fig 5A). Meta-regression (coefficient 0.37 (0.70 to 0.05); P=0.03) (appendix 16, p276) and stratified analyses (appendix 16, p200-206) suggested stronger associations for younger versus older adolescents (<16 years v 16 years), but no moderation effects were by social media category (P=0.13) or study setting (P=0.49). Few studies assessed associations for time spent on social media (appendix 16, p207).

Forest plots for association between frequency of social media use and A) sexual risk behaviour, B) gambling, C) anti-social behaviour, and D) multiple risk behaviours. (A) Forest plot for binary exposure (frequent/at all v infrequent/not at all) and binary/continuous sexual risk behaviour outcome meta-analysis, with OR used as common metric. N=47280. (B) Forest plot for binary exposure (frequent/at all v infrequent/not at all) and binary/continuous gambling outcome meta-analysis, with OR used as common metric. N=26537. (C) Forest plot for binary exposure (frequent/at all v infrequent/not at all) and binary/continuous anti-social behaviour outcome meta-analysis, with OR used as common metric. N=54993. (D) Forest plot for binary exposure (frequent/at all v infrequent/not at all) and binary/continuous multiple risk behaviours outcome meta-analysis, with OR used as common metric. N=43571. CI=confidence interval; n=Number of study participants; OR=odds ratio; RoB=Risk of bias; SM=Social media; SNS=Social networking sites

After excluding one study that had inconsistent findings, across all exposures investigated, all six studies investigating gambling reported harmful associations (appendix 16, p208). Frequent or at all use (v infrequent or not at all) was associated with increased gambling (not via social media) (odds ratio 2.84 (95% confidence interval 2.04 to 3.97); I2=85.6%; n=26537) (fig 5B). On differentiation by social media category, a relatively large association was found for online gambling via social media (3.22 (2.32 to 4.49)), however, associations were not present for social networking sites and general social media (appendix 16, p211).

Across all exposures investigated, all 16 studies (43.8% low/moderate risk of bias) that investigated anti-social behaviour showed harmful associations (appendix 16, p212). Frequent or at all use (v infrequent or not at all) was associated with increased anti-social behaviour (eg, bullying, physical assault, and aggressive/delinquent behaviour) (odds ratio 1.73 (1.44 to 2.06); I2=93.3%; n=54993) (fig 5C), with time spent similarly associated with increased risk (appendix 16, p214). No subgroup differences were noted (appendix 16, p215-217).

For inadequate physical activity, after excluding three studies with inconsistent findings, 36.4% of studies (n=4/11; 72.7% low/moderate risk of bias) reported harmful associations across all exposures investigated (appendix 16, p218). No association between time spent on social media (assessed on a continuous scale) and adolescent engagement in physical activity was seen (Std.Beta 0.00 (95% confidence interval 0.02 to 0.01); I2=59.8%; n=37417) (appendix 16, p219), with no important differences across subgroups (appendix 16, p220-222).

Across all exposures investigated, all 13 studies (including four randomised controlled trials: two rated low risk of bias and two some concerns) that investigated unhealthy dietary behaviour showed harmful associations, with most at low risk of bias (61.5%) (appendix 16, p223). Exposure to health risk behaviour content (specifically marketer generated content) was associated with increased consumption of unhealthy food (odds ratio 2.48 (95% confidence interval 2.08 to 2.97); I2=0.00%; n=7892) when compared with adolescents who had no exposure (appendix 16, p224-225).

For multiple risk behaviours, all nine studies showed harmful associations across all exposures investigated (appendix 16, p226). The pooled odds ratio for frequent and at all social media use (v infrequent and not at all) was 1.75 ((95% confidence interval 1.30 to 2.35); I2=97.9%; n=43571) (fig 5D), but the few studies precluded stratification.

For electronic nicotine delivery system use, associations were stronger for cohort study datapoints (odds ratio 2.13 (95% confidence interval 1.72 to 2.64) v 1.43 (1.20 to 1.69) for cross-sectional datapoints; P=0.004) (appendix 16, p228) but no clear differences were seen for other outcomes (appendix 16, p229-240). Although based on few studies, for unhealthy dietary behaviour a stronger association was found for the randomised controlled trial datapoint versus for the cross-sectional datapoints (3.21 (1.63 to 6.30) v 2.48 (2.08 to 2.97); P=0.44) (appendix 16, p241).

When stratifying by adjustment for critical confounding domains, no clear differences were identified (appendix 16, p242-253), with some exceptions. Associations were stronger for unadjusted versus adjusted datapoints for exposure to content displaying health risk behaviour and alcohol use (Std.Beta 0.28 (0.14 to 0.43) v 0.07 (0.03 to 0.12); P=0.008) and for frequent (v infrequent) social media use and alcohol use (odds ratio 1.54 (95% confidence interval 1.36 to 1.78) v 1.34 (1.24 to 1.44); P=0.06) (appendix 16, p254-255).

For alcohol use, effect sizes were generally stronger for moderate and high risk of bias datapoints (v low) (appendix 16, p256-257), excluding time spent (2 v <2 h per day) and exposure to health risk behaviour content (v no exposure) where low (compared with moderate and high) risk of bias datapoints displayed stronger associations (appendix 16, p258-259). For drug use and sexual risk and anti-social behaviour, no differences were detectable or low/moderate risk of bias datapoints showed stronger associations (compared with high) (appendix 16, p260-264). For tobacco use and gambling, stronger associations were found for high risk of bias datapoints or no clear differences were identified (appendix 16, p265-267). No clear differences by risk of bias were observed for the remaining outcomes (appendix 16, p268-269).

When we excluded datapoints that overlapped the age range of 10-19 years, a marginal reduction in effect size (appendix 16, p270) or no important differences were noted (appendix 16, p271-274).

Funnel plots and Eggers test results suggested some publication bias in the meta-analysis investigating frequent or at all social media use (v infrequent or not at all) and sexual risk behaviours (P=0.04; bias towards the null) (appendix 17). Insufficient data precluded investigation of other outcomes.

As frequency was the most investigated exposure, and continuous and binary exposures reported similar effects, we focused the GRADE assessment on the binary exposure of frequency of use. We report harmful effects on alcohol use with low certainty, and with drug, tobacco, electronic nicotine delivery system use, sexual risk behaviours, gambling, and multiple risk behaviours with very low certainty.

We conducted a post-hoc GRADE assessment for exposure to content displaying health risk behaviour (v no exposure) and unhealthy dietary behaviour because of the substantial difference in quality of evidence observed (four randomised controlled trials); we report moderate GRADE certainty (table 1, appendix 18).59

Condensed summary of findings and certainty of evidence (as per GRADE)

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Social media use and health risk behaviours in young people: systematic review and meta-analysis - The BMJ

GLASGOW, Scotland A new study highlights a concerning link between adolescents frequently using social media and risky decision-making that could put their lives in danger. With millions of young people scrolling through Instagram, Facebook, TikTok and other platforms on a daily basis, the effects of these platforms are far-reaching.

Social media, a vibrant mix of content sharing, social networking, and blogging, has become a cornerstone of modern communication, especially among teenagers. Its not just about staying connected; it offers a sense of freedom and belonging. The World Health Organization even recognizes its power in promoting health, noting its role in fostering healthy lifestyles, accessibility to health information, and emotional support.

However, its not all positive. Researchers from the University of Glasgow found a strong association between regular social media use and various risky health behaviors among young children and teens. These include increased underage drinking, drug use, and smoking, as well as antisocial behavior, such as unsafe sexual activity and gambling.

But how does this happen? The study points to several factors:

The study, which analyzed data from 1.4 million adolescents between the ages of 10 and 19 from 1997 to 2022, found that exposure to social media content promoting risky activities, such as alcohol advertisements, showed the most substantial evidence of harm. This was particularly evident in the cases of alcohol consumption and unhealthy eating habits.

Key findings revealed that spending a minimum of two hours daily on social media doubled the likelihood of alcohol consumption compared to those who used it for less than two hours. Published in The BMJ, the study also highlighted that frequent or daily social media usage increased the probability of alcohol consumption by 48 percent, drug use by 28 percent, and tobacco use by 85 percent, compared to those who used social media infrequently or not on a daily basis.

Additionally, regular social media engagement was linked to a 77-percent rise in risky sexual behaviors, such as sexting, transactional sex, and inconsistent condom use, as well as a 73-percent increase in antisocial activities like bullying, physical assault, and aggressive or delinquent behavior. The study also noted that frequent social media users were almost three times more likely to engage in gambling compared to their peers who used social media infrequently or not every day.

Experimental and risk-taking behaviors are an inherent part of adolescence, the study authors write in a media release. However, as safeguards for a digital world are still evolving, precaution across academic, governmental, health and educational sectors may be warranted before the risks of adolescents use of social media is fully understood.

The study underlines the urgent need for more targeted research, particularly in low and middle-income countries. It also calls for a multi-pronged approach to safeguarding young people online, including better digital literacy education and more robust online safety policies.

South West News Service writer Isobel Williams contributed to this report.

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Kids who use social media more prone to making dangerous decisions - Study Finds

Study design and setting

This is an institution-based, descriptive, cross-sectional study. It was conducted at the Faculty of Medicine, University of Khartoum, Khartoum state, Republic of Sudan in January, February, and March 2021.

We included all undergraduate medical students at the faculty of medicine, university of Khartoum (the total population was 2016 students from 6 classes, both males and females). We excluded students unwilling to participate.

With a total population (N) of 2016 and a level of precision (d) of 0.05; a sample size (n) of 333 students was calculated using the formula:(n=N/1+(Ntimes d^2 )). The sample was selected using proportionate simple random sampling.

The total population was divided into 6 classes that correspond to the academic years. The size of the student sample from each class was proportionate to the size of the class (56 from 1st year class, 54 from 2nd year class, 55 from 3rd year class, 59 from 4th year class, 57 from 5th year class, 52 from 6th year class). Students from each class were selected by simple random sampling.

Data was collected using an online, self-administered structured questionnaire (Google form) which consisted of sociodemographic data, social networking intensity (SNI) scale, and fear of missing out (FOMO) scale. Names were not included to ensure confidentiality.

The questionnaire (Supplementary 1) was developed for this study and composed of 23 items, divided into 3 sections. The socio-demographic characteristics section was composed of 6 variables (AgeSexBatchPlace of residenceMarital statusMonthly income) (Table 1). Participants were asked about the way they access the internet through most of the time (smartphones or laptops), and how they connect to the internet (Table 1). In social networking intensity section, a scale of 5 items was used to assess the level of social networking intensity (SNI) of each participant, each item used a 5-point Likert scale (1=Not at all true for me, 2=Slightly true for me, 3=Moderately true for me, 4=Very true for me, and 5=Extremely true for me), SNI score for each individual was calculated by summation of the five items. Scores ranged between 5 and 25 (5 represents low SNI and 25 represents the highest level of SNI). According to Salehan and Negahban, this scale has a good internal consistency, with a Cronbach alpha coefficient of 0.88 [8]. In our study, the Cronbach alpha coefficient for the SNI scale was 0.84 which suggests a good internal consistency and reliability for the scale regarding our sample (Table 2). SNI scores were classified into three grades: low (scores 510), moderate (scores 1119), and high (scores 2025).

In fear of missing out section, a scale of 10 items was used to assess FOMO among the participants, each item used a 5-point Likert scale (1=Not at all true for me, 2=Slightly true for me, 3=Moderately true for me, 4=Very true for me, and 5=Extremely true for me), FOMO score for each participant was calculated by summation of the ten items. Scores ranged between 10 and 50 (10 represents low FOMO and 50 represents the highest level of FOMO). According to Przybylski, Murayama, DeHann, and GladWell (2013), this scale has a good internal consistency, with a Cronbach alpha coefficient of 0.89 [9]. In our study; we had a Cronbarch alpha coefficient of 0.88 which suggests reliable results with good internal consistency for the FOMO scale regarding our sample (Table 3). FOMO scores were classified into three grades: low (scores 1020), moderate (scores 2139) and high (scores 4050).

Statistical Package for Social Science 26 (SPSS-26) software was used for data entry and analysis. Simple descriptive statistics were used to determine the frequencies and percentages of the different variables. Cronbach's alpha coefficient was calculated for the two scales to determine their internal consistency. Pearson correlation coefficient was used to assess the association between SNI & FOMO. Linear regression analysis was used to describe the relation between FOMO and SNI.

Independent t-test and one-way ANOVA were used to examine the associations and differences related to the socio-demographic groups.

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Social networking and fear of missing out (FOMO) among medical ... - BMC Psychology

Meta, the social media company that owns Facebook and Instagram, said Thursday that this year it has taken down five networks of fake accounts originating in China that aimed to influence politics in other countries. Justin Sullivan/Getty Images hide caption

Meta, the social media company that owns Facebook and Instagram, said Thursday that this year it has taken down five networks of fake accounts originating in China that aimed to influence politics in other countries.

China is stepping up efforts to manipulate people in other countries on social media, becoming the third most common source of foreign influence operations, behind Russia and Iran, according to Meta, the parent company of Facebook and Instagram.

Meta has taken down five Chinese networks of fake accounts in 2023, the most of any country this year, the company said in a new report published on Thursday. That's a significant increase from 2019, when Meta first removed a campaign based in China, although the country's efforts over the years haven't gained much traction.

"This is the most notable change in the threat landscape compared with 2020," said Ben Nimmo, Meta's global threat intelligence lead.

The targets of the Chinese operations that Meta has disrupted include people in sub-Saharan Africa, Central Asia, Europe and the United States. The campaigns vary widely in how they work, but the focus tends to be on promoting Chinese interests, from defending Beijing's human rights record to attacking government critics, Nimmo said.

"There's a very kind of global mandate there. And they are using many different tactics. So we've seen small operations that try and build personas. We've seen larger operations using large, clunky, sort of spammy networks," he said. "The common denominator, other than origin in China, is really that they're all struggling to get any kind of authentic audience."

Most recently, Meta took down two China-based operations in the third quarter of this year. One was a network of around 4,800 Facebook accounts impersonating Americans and posting about domestic politics and U.S.-China relations.

Using fake names and profile pictures copied from elsewhere online, the accounts some of which also operated similar accounts on X, formerly known as Twitter copied and pasted posts on X from American politicians. The copying spanned political parties, including Democrats Rep. Nancy Pelosi of California, Sen. Mark Kelly of Arizona and Michigan Gov. Gretchen Whitmer, as well as Republicans Rep. Jim Jordan of Ohio, Sen. Marsha Blackburn of Tennessee and the presidential campaign war room of Florida Gov. Ron DeSantis.

"It's unclear whether this approach was designed to amplify partisan tensions, build audiences among these politicians' supporters, or to make fake accounts sharing authentic content appear more genuine," Meta said in its report.

The posts were obviously copied, with some including giveaways like "RT," indicating a retweet, and the @ symbol used before an X username. Some of the accounts reshared posts from X owner Elon Musk, as well as links to news articles and Facebook posts from real people. Meta said it removed the accounts before they were able to get engagement from real users.

The other network that Meta took down was smaller but more sophisticated. It consisted of 13 Facebook accounts and seven groups mainly targeting Tibet and India. The accounts posed as journalists, lawyers and human rights activists. Some also operated accounts using the same names and profile pictures on X.

They posted about regional news, sports and culture, criticized the Dalai Lama and accused the Indian government of corruption while praising India's army, athletes and scientific achievement. A handful posed as Americans and shared links to U.S. news outlets. Meta said about 1,400 accounts joined one of the groups before the groups were taken down.

Nimmo said the contrast in the two campaigns shows the range of tactics that China-based networks employ. "There isn't a single playbook which would apply to Chinese [influence operations]," he said.

Meta didn't attribute either network to a specific actor in China. Previously, the company has attributed other disrupted operations to the Chinese government, IT firms and Chinese law enforcement.

With a slew of elections on tap in 2024, including in the U.S., Taiwan, India and the European Union, Chinese operations may "pivot" to target discussions of relations with China in those places, Nimmo said. That will add to expected operations by Russia and Iran.

"Because we've already seen threat actors trying to hijack partisan narratives, we hope that people will try to be deliberate when engaging with political content across the internet," he said. "For political groups, it's important to be aware that heightened partisan tensions can play into the hands of foreign threat actors."

Russia, which Meta says remains the most prolific source of coordinated influence operations, has mainly been focused on undermining international support for Ukraine since its February 2022 invasion of that country. But recently, a Russian operation known as Doppelganger that impersonates news outlets has launched a new set of websites focused on American and European politics and elections, using names including Election Watch, Truthgate and 50 States of Lies.

"Much of their content appears to have been copy-pasted from mainstream U.S. news outlets and altered to question U.S. democracy," Nimmo said. "In addition, soon after the Hamas terrorist attack in Israel, we saw these websites begin portraying the war as proof of American decline. At least one website claimed that Ukraine supplied Hamas with weapons. Other websites in the cluster focused on politics and migration in France and Germany."

Meta said it is blocking those websites from its platforms and sharing the full list of Doppelganger-linked domains with other companies.

After Russian efforts to influence the 2016 U.S. presidential election brought attention to the risks of foreign interference online, Meta and other tech companies came together with civil society groups, researchers and federal agencies to harden online platforms against such campaigns by sharing information, including tips about threats. But those efforts have recently come under legal and political pressure from Republicans who claim they amount to illegal censorship, and this coordination has begun to break down.

In its report, Meta said the U.S. government has "paused" sharing information about foreign election interference since July. That's when a federal judge issued an injunction barring federal agencies from communicating with social media platforms about most content. The injunction has been put on hold while the Supreme Court hears the case, but it has already had a widespread chilling effect.

Nathaniel Gleicher, Meta's head of security policy, said the company continues to share information about threats it uncovers with the government and other partners.

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Meta warns that China is stepping up its online social media ... - NPR

This is a tough subject.

As a trainer, I have a really hard time with social media and the time it takes our kids away from real time with their horsesthe hours and hours wasted on screen time that could be spent in the barn.

Ive lost track of how many times Ive heard I just didnt have time, or I didnt get to it, or I forgot from students. The practice and grooming and loving comes second to the screen, and the other things they have to dohomework, chores, jobsbecome more important than just being quiet with their horse. Im not sure kids even enjoy the little moments anymorethe inviting smell of the barn, the butterfly that their horse is watching, the loving look from their horse.

And then there are the posts. The endless no thought behind what is real, what is appropriate, what is hurtful, and what is just not necessary posts filling up their social media feeds are devastating to our sport.

Take the posts about horses. Why should everyone know something about someone elses horse? Why would anyone want to make fun of another persons horse? Why is it okay to bend the truth about things and for others to read those fabrications as if they are facts?

This sport is so special because of what it can teach a young personthe responsibility, the love, the grit, the hard work, the list goes on and on. As a trainer you need it all to go hand in hand so the rider can be the best of the best. And in real time, not on a screen or in a post.

Im not sure its even possible anymore with the time crunch of this increasingly digital world. Time spent at the barn was once freely given and is near impossible to duplicate. The lessons in patience and observation athletes would learn from watching their horse play with a butterfly or while driving across the country to a show cannot be taught. Its acquired through experience and is what fuels the passion, the drive, the desire to be the best.

You simply cannot replace the real time required, nor can you take back the images and the posts that they consume online.

As a trainer, I know there has to be a balance. I know social media isnt going away. But I feel the balance is too hard for a kid to grasp on their own and that less screen time can only mean more when it comes to riding.

As a judge, I cant help but think that screens are holding up the ring. I spend many minutes and hours in the judges box waiting for a competitor in the ring, but when I look up to see what everyone is doing ringside, theyre inevitably on their phones.

What would they be doing if their phone wasnt there to distract them? Would they be helping others? Would they be working and seeing others work, pushing our young ones to do as they see?

I wonder how we keep our sport alive and thriving in the digital age. The job of a judge is to rate and review the class the best you canand, in turn, you hope to inspire riders and the sport to grow. When you walk into the ring and the judge verifies where you are compared to the competition that day, it drives you to work and get better, to move to another division, etc.

But when you are distracted by a constant stream of social posts that are maybe not entirely correct or dont show the whole picture, what toll does that take, particularly on the mental health of children?

If phones werent within reach of every fingertip, would kids watch each others trips more? Would they learn from sitting at the ring? Would they actually see the judges perspective? Would there be less negativity about the judging if they watched all the trips in a class and understood what mistakes were made and why the ribbons went way they did? That is the piece that you cant always teach, but you get when youre in real time!

As a mom, the worst hat I wear is my social media police hat. You want your kid to be social and have friends and not be weird. You also want your kid to be their own person and to do what they love and contribute positively to society.

I know, as a mom, that working with a live animal and all that comes with that teaches our kids more, Id argue, than any other sport. Beyond the grit and determination and strategy of competition, horses teach empathy and resilience, failure and sacrifice, patience and perseverance. Horses teach ALL life lessons. But Im not sure kids learn from it now like we did when we had literally nothing else to do.

I have always pushed my kids to not have any screen time at all and, I can tell you, that doesnt work. So then you try to understand their side and get involvedand you experience the feeling it gives you and you dont want a kid to feel that way.

You hear that social media can help kids make connections they may not have otherwise. It can give them an identity in the horse world and help promote them. Networking is not only word of mouth anymore. Its social media spreading the word.

And, at the same time, it means they now have to grow up so fast! They have to understand how not to compare themselves to others, how not to get hurt by posts that arent truly directed at themhow to stay real in a virtual world.

As a parent, you have to teach them all of it before they go down a bad path of rage and devastation that social media can lead to. Kids dont get to live and learn anymore. They have to learn first. And as parents, we have to give them the tools to navigate that.

I know social media isnt going away. I also know itd be better for everyoneour kids, our horses, our sportif less time was lost to screens and if we we used social media more as a tool than a crutch.

Horses teach the same life lessonsand in a better wayall by themselves.

Dana Hart Callanan is a successful hunter, jumper and equitation coach, an R judge, and a sales broker. In this column, she answerscommon questions about A level sport.

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Q&A: What Is Social Media Doing to Our Kids and Our Sport? - Horse Network