Bias can be defined as selective favoritism exhibited by human beings when posed with a task of decision making across multiple options. Online communities present plenty of decision making opportunities to their users. Users exhibit biases in their attachments, voting and ratings and other tasks of decision making. We study bias amongst microblog users due to the value of an author’s name. We describe the relationship between name value bias and number of followers, and cluster authors and readers based on patterns of bias they receive and exhibit, respectively. For authors we show that content from known names (e.g., @CNN) is rated artificially high, while content from unknown names is rated artificially low. For readers, our results indicate that there are two types: slightly biased, heavily biased. A subsequent analysis of Twitter author names revealed attributes of names that underlie this bias, including effects for gender, type of name (individual versus organization), and degree of topical relevance. We discuss how our work can be instructive to content distributors and search engines in leveraging and presenting microblog content.