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Thesurvey data reveals a distinct pattern in social media use by socio-economic status. Teens from less well-off households (those earning less than $50,000) are more likely than others to say they use Facebook the most: 49% of these teens say they use it most often, compared with 37% of teens from somewhat wealthier families (those earning $50,000 or more).

Teens from more affluent households are somewhat more likely than those from the least affluent homes to say they visit Snapchat most often, with 14% of those from families earning more than $75,000 saying Snapchat is their top site, compared with 7% of those whose families earn less than $30,000 annually. Twitter shows a similar pattern by income, with the wealthiest teens using Twitter more than their least well-to-do peers. It should be noted that some of these differences may be artifacts of differences in use of these sites by these different subgroups of teens.

As American teens adopt smartphones, they have a variety of methods for communication and sharing at their disposal. Texting is an especially important mode of communication for many teens. Some 88% of teens have or have access to cell phones or smartphones and 90% of those teens with phones exchange texts. A typical teen sends and receives 30 texts per day2

And teens are not simply sending messages through the texting system that telephone companies offer. Some 73% of teens have access to smartphones and among them messaging apps like Kik or WhatsApp have caught on. Fully 33% of teens with phones have such apps. And Hispanic and African-American youth with phones are substantially more likely to use messaging apps, with 46% of Hispanic and 47% of African-American teens using a messaging app compared with 24% of white teens.

Data for this report was collected for Pew Research Center. The survey was administered online by the GfK Group using its KnowledgePanel, in English and Spanish, to a nationally representative sample of over 1,060 teens ages 13 to 17 and a parent or guardian from September 25 to October 9, 2014 and February 10 to March 16, 2015. In the fall, 1016 parent-teen pairs were interviewed. The survey was re-opened in the spring and 44 pairs were added to the sample. For more on the methods for this study, please visit the Methods section at the end of this report.

New research by the Web Foundation shows that the dramatic spread of mobile phones is not enough to get women online, or to achieve empowerment of women through technology. The study, based on a survey of thousands of poor urban men and women across nine developing countries*, found that while nearly all women and men own a phone, women are still nearly 50% less likely to access the Internet than men in the same communities, with Internet use reported by just 37% of women surveyed. Once online, women are 30-50% less likely than men to use the Internet to increase their income or participate in public life.

*The survey was conducted by Ipsos-Mori in ten countries: Cameroon, Colombia, Egypt, India, Indonesia, Kenya, Mozambique, Nigeria, Philippines, Uganda. The global report launched on 21st October will include all countries except Egypt, which will be published at a later date.

Overall, due to the poor quality of previous research on the prevalence of SNS addiction in terms of sampling, study design, measurement, and cut-off score employed, it is premature to draw conclusions about prevalence and relevant risk factor of SNS addiction.

Several screening instruments of SNS addiction have appeared in the literature. Researchers investigating SNS addiction have first and foremost focused on Facebook addiction, while some focus on other social networks, or SNSs in general. Table 1 briefly presents relevant screening instruments.

FDQ is an eight-item questionnaire that measures Facebook dependence [20]. The item pool is based on an Internet addiction scale [30], and measures control, satisfaction, time of use and efforts to reduce it, worries, concern, and other activities involved in Facebook. The response format is dichotomized (yes/no), where the cut-score is endorsement of at least five items. FDQ was constructed in a Peruvian sample of 418 students. Statistical methodology involved calculation of internal consistency (0.67).

ATS is a three-item questionnaire developed by Wilson and colleagues [32]. Anchored in general addiction theory and research on excessive text messaging/instant messaging. ATS operationalizes SNS addiction as being comprised of three core addiction criteria: salience, loss of control, and withdrawal. All items are scored on a seven-point scale that ranges from strongly disagree to strongly agree. Cut-off scores are not suggested. The scale was constructed in an Australian sample of 201 students. Measure of internal consistency was 0.76.

Sample and statistical methodology used in the initial scale-construction studies entail common drawbacks such as small non-representative cross-sectional study designs. Because of their recent developments, their psychometric properties have primarily been tested and reserved to these initial studies so far. Also, very few scales come with suggested cut-score for categorizing SNS addicts.

Apps exist to help one cut down on time spent on social media and to eliminate digital distractions. By downloading such apps (ColdTurkey, SelfControl, Freedom) the SNS user can block the sites one like to avoid. It is also possible to install settings on SNSs that give time-fixed updates (e.g., every second hour). As people very often have excess to their social network sites via their smartphones, they can turn it off or set it on flight or silent mode when they do not wish to be disrupted. Other practical self-help strategies may pinpoint criteria such as not logging on to social network sites at work or school, leaving the smartphone at work or home, scheduling adequate breaks to visit social network sites, modifying thought patterns while social networking, setting limits and reasonable goals according to other obligations, and committing to offline activities etc. Relaxation techniques to better handle emotional discomfort may also come in handy (e.g., mindfulness) [76].

As leaders, teachers, and parents serve as significant role models, both online and offline, they should lead by example. How they behave, along with the reward system they serve, is of crucial importance for toning down or intensifying excessive social networking among their employees, students, and children [60].

You can download an Errata sheet [PDF - 145 KB] describing a minor typographical error identified in the original printing of the 2015-2020 Dietary Guidelines for Americans. This error has been corrected in the online versions.

Accounts of the Arab Spring have frequently credited Twitter, Facebook, and other social network sites with helping the protests self-organize [13]. Indeed, there are many reasons to expect that social media did play a role. When deciding whether or not to protest, individuals have to estimate the risk of arrest or violent state repression, and they have to weigh that cost against the potential benefits of any change in policy that may result. They are primarily interested in how many other individuals are going to protest and whether those participants are first-time protesters or not [14]. Protesting en masse decreases the chance an individual will personally experience state violence and increases the probability of achieving policy change. Individuals wanting to protest are therefore strongly incentivized to coordinate their protest action with others.

Social media can make it easier to protest because it lowers the barriers required to coordinate, making it easier to know if others will protest and whether or not they are habitual protesters. Joining a social media platform requires many fewer resources than establishing a newspaper, running a television station, or opening a civil society organization [15], meaning many more people can produce information seen by others. For example, tweets can be composed and read using a basic mobile phone, and blogs only require access to an internet connection. Social media also facilitates connections between people who otherwise would not come into contact [16], greatly increasing the number of people that know about an event. While governments can also use social media to monitor and repress their citizens [17, 18], the lower barriers to entry and broadcast-like features of social media give individuals more power to coordinate than they have without online social networks.

Here, we quantitatively test the hypothesis that social media usage correlates with subsequent protests, using longitudinal data from the Arab Spring. Though individuals use many online networks to organize protest, we focus on the social media site Twitter for three reasons. First, it has become a tool for citizens to gather and disseminate information in information-scarce environments such as authoritarian regimes. It therefore is a critical component of many protest movements, starting with the 2009 Iran election protests and continuing through the Ukraine civil war. Second, it provides some of the best temporal resolution of any data source. It is therefore one of the few sources available to researchers interested in quickly-changing processes such as protests. Third, state actors belatedly realized the power of social media, making social media an attractive tool for anyone seeking independent information; the content contained in social media therefore more closely reflected the offline world than did official news sources [19].

To determine that social media were used to coordinate protest, we measure the daily number of protests across 16 countries in the Middle East and North Africa from November 1st, 2010 through December 31st, 2011. These data come from a publicly-available machine-coded events dataset, the Global Database of Events, Location, and Tone (GDELT). GDELT machine-reads American and foreign newspapers and extracts the events each article is about [20]. Figure 1 shows the daily measure of protests in a high-protest country (Egypt) and low-protest one (Qatar) as well as the average level of protest per country in our study. In the Supplementary Materials (Additional file 1), we show that this measure correlates with hand-coded datasets of protest; others have shown that it correlates with other machine-coded data [21].

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