College of Information Studies

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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

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Author: search.filters.author.Golbeck, Jennifer

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    Benford’s Law applies to word frequency rank in English, German, French, Spanish, and Italian
    (PLoS, 2023-09-14) Golbeck, Jennifer
    Benford’s Law states that, in many real-world data sets, the frequency of numbers’ first digits is predicted by the formula log(1 + (1/d)). Numbers beginning with a 1 occur roughly 30% of the time, and are six times more common than numbers beginning with a 9. We show that Benford’s Law applies to the the frequency rank of words in English, German, French, Spanish, and Italian. We calculated the frequency rank of words in the Google Ngram Viewer corpora. Then, using the first significant digit of the frequency rank, we found the FSD distribution adhered to the expected Benford’s Law distribution. Over a series of additional corpora from sources ranging from news to books to social media and across the languages studied, we consistently found adherence to Benford’s Law. Furthermore, at the user-level on social media, we found Benford’s Law holds for the vast majority of users’ collected posts and significant deviations from Benford’s Law tends to be a mark of spam bots.
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    Environmental Factors Affecting Where People Geocache
    (MDPI, 2016-04-12) Golbeck, Jennifer; Neustaedter, Carman
    Outdoor leisure activities are important for public health as well as family cohesiveness, yet environmental factors may easily affect someone’s ability to participate in such activities. We explored this with a focus on the social web-based treasure hunt game called Geocaching. We collected data on all US and Canadian geocaches from OpenCaching.com and conducted an online survey with twenty geocachers as a follow-up to our data analysis. Data analysis showed that geocaches were more often found in areas that were wealthier, better educated, younger, and more urban, and had higher population density and better weather. Survey results showed similar trends: Most people actively thought about where they would cache and tried to minimize risks, despite cache hiders thinking less about these concerns. These results further emphasize the importance of environmental factors when it comes to participation in outdoor activities and leads to Human–Computer Interaction design implications for location-based online social activities.
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    User Perception of Facebook App Data Access: A Comparison of Methods and Privacy Concerns
    (MDPI, 2016-03-25) Golbeck, Jennifer; Mauriello, Matthew Louis
    Users share vast amounts of personal information online, but are they fully aware of what information they are sharing and with whom? In this paper, we focused on Facebook apps and set out to understand how concerned users are about privacy and how well-informed they are about what personal data apps can access. We found that initially, subjects were generally under-informed about what data apps could access from their profiles. After viewing additional information about these permissions, subjects’ concern about privacy on Facebook increased. Subjects’ understanding of what data apps were able to access increased, although even after receiving explicit information on the topic, many subjects still did not fully understand the extent to which apps could access their data.
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    Benford’s Law Applies to Online Social Networks
    (PLOS (Public Library of Science), 2015-08-26) Golbeck, Jennifer
    Benford’s Law states that, in naturally occurring systems, the frequency of numbers’ first digits is not evenly distributed. Numbers beginning with a 1 occur roughly 30%of the time, and are six times more common than numbers beginning with a 9.We show that Benford’s Law applies to social and behavioral features of users in online social networks. Using social data from five major social networks (Facebook, Twitter, Google Plus, Pinterest, and LiveJournal), we show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford’s Law. The same is true for the number of posts users make.We extend this to egocentric networks, showing that friend counts among the people in an individual’s social network also follows the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets.