2018- Working Papers: Technology and Information Systems

Theories of personal branding are built on the idea that each individual should be aware of the persona she presents to the world. Nowadays, as social interactions are increasingly shifting to the online arena, users of social platforms are presented with many new opportunities and technology-enabled tools by which they can construct their online personas. A powerful type of tool that has emerged in this ecosystem is the ability to reiterate a friend's activity, that is, to redistribute an exact copy of the content that he or she has posted online (e.g., words, videos, or pictures) and to incorporate it into one's own online image. In this work, we examine how users employ reiteration tools when presenting themselves and shaping their online presence. We focus on retweeting behavior on Twitter and study the spectrum of topics that users choose to reiterate. We hypothesize that users' retweeting behavior will show patterns that are theorized to characterize effective personal branding strategies: Specifically, when reiterating content produced by others, a user will maintain a persona that is consistent with the persona portrayed in her self-produced tweets. We analyze data taken from Twitter over a period of 6 months in 2016, with regard to 3,388 non-expert users and 464 expert users and the users whom they followed. We use LDA topic modeling to derive the topics in each user's self-produced tweets and retweets. We find that users' retweets tend to focus on the topics that they address in their self-produced tweets, instead of adding new topics. Further, we find that a user's retweets do remarkably little to alter the distribution of topics she discusses in her self-produced tweets. Finally, we find that this tendency is more prominent among "expert" users, i.e., professional bloggers who are particularly likely to use Twitter as a personal branding tool. A rigorous identification strategy lends support to the proposition that the observed effects are indeed driven by image-related considerations rather than by alternative factors known to influence retweeting behavior, such as exposure bias (a phenomenon associated with the formation of echo chambers), need for uniqueness, and social dynamics on the Twitter platform.

The big data revolution has transformed more and more areas of business, from banner advertising through product recommendations to sales predictions. Surprisingly, one of the areas that is still lagging behind in adopting analytics is academia. While academic researchers are leading the way in generating new methods and algorithms, when it comes to predicting and ranking academic research, current academic decisions like hiring, tenure and prizes are mostly very subjective. Previous literature, from Kuhn (1962), who discussed the importance of novelty to the development of the scientific process, to a recent work by Boudreau et al. (2014), which shows how innovativeness of research (which they measure as “intellectual distance”) affects the likelihood that it is funded, has discussed the important role of novelty in science. This research aims to continue this stream of literature and to build a set of quantitative novelty measures that could support the academic decision making process. These measurements will be embedded in the models to estimate their predictive efficacy when predicting future academic success of papers and scholars.