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Flags are important national symbols that have transcended into the digital world with inclusion in the Unicode character set. Despite their significance, there is little information about their role in online communication. This article examines the role of flag emoji in political communication online by analyzing 640,676 tweets by the most important political parties and Members of Parliament in Germany and the United States. We find that national flags are frequently used in political communication and are mostly used in-line with political ideology. As off-line, flag emoji usage in online communication is associated with external events of national importance. This association is stronger in the United States than in Germany. The results also reveal that the presence of the national flag emoji is associated with significantly higher engagement in Germany irrespective of party, whereas it is associated with slightly higher engagement for politicians of the Republican party and slightly lower engagement for Democrats in the United States. Implications of the results and future research directions are discussed.

Passwords commonly contain personal information. However, there is limited awareness about its detrimental effect on the user’s online security. Current password meters do not take into account personal information and, therefore, their users are susceptible to targeted password guessing. In this paper, we present the MoiPrivacy password meter, that extends a neural network- and heuristic-based approach and considers a user’s personal information, while calculating the password strength and feedback. To do so, we analyzed the type of personal information used in passwords through an online survey (n = 62). We conducted a second user study (n = 49) for evaluating the MoiPrivacy browser extension. Our results show that MoiPrivacy significantly limits the inclusion of personal information in passwords.

Many applications of geotagged content are predicated on the concept of localness (e.g., local restaurant recommendation, mining social media for local perspectives on an issue). However, definitions of who is a "local" in a given area are typically informal and ad-hoc and, as a result, approaches for localness assessment that have been used in the past have not been formally validated. In this paper, we begin the process of addressing these gaps in the literature. Specifically, we (1) formalize definitions of "local" using themes identified in a 30-paper literature review, (2) develop the first ground truth localness dataset consisting of 132 Twitter users and 58,945 place-tagged tweets, and (3) use this dataset to evaluate existing localness assessment approaches. Our results provide important methodological guidance to the large body of research and practice that depends on the concept of localness and suggest means by which localness assessment can be improved.