Predicting Rising Follower Counts on Twitter Using Profile Information

When evaluating the cause of one's popularity on Twitter, one thing is considered to be the main driver: Many tweets. There is debate about the kind of tweet one should publish, but little beyond tweets. Of particular interest is the information provided by each Twitter user's profile page. One of the features are the given names on those profiles. Studies on psychology and economics identified correlations of the first name to, e.g., one's school marks or chances of getting a job interview in the US. Therefore, we are interested in the influence of those profile information on the follower count. We addressed this question by analyzing the profiles of about 6 Million Twitter users. All profiles are separated into three groups: Users that have a first name, English words, or neither of both in their name field. The assumption is that names and words influence the discoverability of a user and subsequently his/her follower count. We propose a classifier that labels users who will increase their follower count within a month by applying different models based on the user's group. The classifiers are evaluated with the area under the receiver operator curve score and achieves a score above 0.800.Comment: 10 pages, 3 figures, 8 tables, WebSci '17, June 25--28, 2017, Troy, NY, US

Integrated assessment of biological invasions

An assessment of the consequences of biological invasions and of the measures taken against must be at the base of each social decision in this field. Three forms of uncertainty can be distinguished that make such a decision difficult to take: (1) factual uncertainty, which encompasses not only risk, but also unknown probabilities of known consequences, and unknown consequences, (2) individual uncertainty, i.e. insecurity about the values to consider, and about the form how to consider them, and (3) social actor uncertainty, i.e. uncertainty about the social actors to consider and how to do it. This paper furnishes axiomatic reflections about the difficulties of assessments integrating these three uncertainties. Using this analytical separation, it restructures two main assessment techniques, and herewith shows the main differences between cost-benefit-analysis and multi-criteria decision aid in supporting public decisions about biological invasions. It is shown that the main difference between cost-benefit-analysis, the classical economic decision support, and multi-criteria decision analysis is less its mono- vs. multi-criteria approach, but its facility to be embedded in a social decision context. With multicriteria decision aid it is more facile to lay open the uncertainties in all three dimensions and to make them an explicit topic for public discourse. Therefore, it seems more suitable as an assessment method for biological invasions. --Biodiversity,Multi-criteria analysis,Uncertainty,Integrated Assessment,Biological Invasion,Cost-benefit analysis