In a high-choice media environment, there are fears that individuals will select media and content that reinforce their existing beliefs and lead to segregation based on interest and/or partisanship. This could lead to partisan echo chambers among those who are politically interested and could contribute to a growing gap in knowledge between those who are politically interested and those who are not. However, the high-choice environment also allows individuals, including those who are politically interested, to consume a wide variety of media, which could lead them to more diverse content and perspectives. This study examines the relationship between political interest as well as media diversity and being caught in an echo chamber (measured by five different variables). Using a nationally representative survey of adult internet users in the United Kingdom (N = 2000), we find that those who are interested in politics and those with diverse media diets tend to avoid echo chambers. This work challenges the impact of echo chambers and tempers fears of partisan segregation since only a small segment of the population are likely to find themselves in an echo chamber. We argue that single media studies and studies which use narrow definitions and measurements of being in an echo chamber are flawed because they do not test the theory in the realistic context of a multiple media environment.

The use of online surveys has grown rapidly in social science and policy research, surpassing more established methods. We argue that a better understanding is needed, especially of the strengths and weaknesses of non‐probability online surveys, which can be conducted relatively quickly and cheaply. We describe two common approaches to non‐probability online surveys—river and panel sampling—and theorize their inherent selection biases: namely, topical self‐selection and economic self‐selection. We conduct an empirical comparison of two river samples (Facebook and web‐based sample) and one panel sample (from a major survey research company) with benchmark data grounded in a comprehensive population registry. The river samples diverge from the benchmark on demographic variables and yield much higher frequencies on non‐demographic variables, even after demographic adjustments; we attribute this to topical self‐selection. The panel sample is closer to the benchmark. When examining the characteristics of a non‐demographic subpopulation, we detect no differences between the river and panel samples. We conclude that non‐probability online surveys do not replace probability surveys, but augment the researcher's toolkit with new digital practices, such as exploratory studies of small and emerging non‐demographic subpopulations.

Hundreds of papers have been published using Twitter data, but few previous papers report the digital divide among Twitter users. British Twitter users are younger, wealthier, and better educated than other Internet users, who in turn are younger, wealthier, and better educated than the off-line British population. American Twitter users are also younger and wealthier than the rest of the population, but they are not better educated. Twitter users are disproportionately members of elites in both countries. Twitter users also differ from other groups in their online activities and their attitudes. These biases and differences have important implications for research based on Twitter data. The unrepresentative characteristics of Twitter users suggest that Twitter data are not suitable for research where representativeness is important, such as forecasting elections or gaining insight into attitudes, sentiments, or activities of large populations. In general, Twitter data seem to be more suitable for corporate use than for social science research.

Sociological studies show that Internet access, skills, uses, and outcomes vary between different population segments. However, we lack differentiated statistical evidence of the social characteristics of users of distinct social media platforms. We address this issue using a representative survey of Great Britain and investigate the social characteristics of six major social media platforms. We find that age and socioeconomic status are driving forces of several—but not all—of these platforms. The findings suggest that no social media platform is representative of the general population. The unrepresentativeness has major implications for research that uses social media as a data source. Social media data cannot be used to generalize to any population other than themselves.