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تسجيل مستخدم جديدClarifying the Role of Distance in Friendships on Twitter: Discovery of a Double Power-Law Relationship
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This study analyzes friendships in online social networks involving geographic distance with a geo-referenced Twitter dataset, which provides the exact distance between corresponding users. We start by introducing a strong definition of friend on Twitter, requiring bidirectional communication. Next, by utilizing geo-tagged mentions delivered by users to determine their locations, we introduce a two-stage distance estimation algorithm. As our main contribution, our study provides the following newly-discovered friendship degree related to the issue of space: The number of friends according to distance follows a double power-law (i.e., a double Pareto law) distribution, indicating that the probability of befriending a particular Twitter user is significantly reduced beyond a certain geographic distance between users, termed the separation point. Our analysis provides much more fine-grained social ties in space, compared to the conventional results showing a homogeneous power-law with distance.
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Studies on friendships in online social networks involving geographic distance have so far relied on the city location provided in users profiles. Consequently, most of the research on friendships have provided accuracy at the city level, at best, to
designate a users location. This study analyzes a Twitter dataset because it provides the exact geographic distance between corresponding users. We start by introducing a strong definition of friend on Twitter (i.e., a definition of bidirectional friendship), requiring bidirectional communication. Next, we utilize geo-tagged mentions delivered by users to determine their locations, where @username is contained anywhere in the body of tweets. To provide analysis results, we first introduce a friend counting algorithm. From the fact that Twitter users are likely to post consecutive tweets in the static mode, we also introduce a two-stage distance estimation algorithm. As the first of our main contributions, we verify that the number of friends of a particular Twitter user follows a well-known power-law distribution (i.e., a Zipfs distribution or a Pareto distribution). Our study also provides the following newly-discovered friendship degree related to the issue of space: The number of friends according to distance follows a double power-law (i.e., a double Pareto law) distribution, indicating that the probability of befriending a particular Twitter user is significantly reduced beyond a certain geographic distance between users, termed the separation point. Our analysis provides concrete evidence that Twitter can be a useful platform for assigning a more accurate scalar value to the degree of friendship between two users.
The Covid-19 pandemic has had a deep impact on the lives of the entire world population, inducing a participated societal debate. As in other contexts, the debate has been the subject of several d/misinformation campaigns; in a quite unprecedented fa
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Many Twitter users are bots. They can be used for spamming, opinion manipulation and online fraud. Recently we discovered the Star Wars botnet, consisting of more than 350,000 bots tweeting random quotations exclusively from Star Wars novels. The bot
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We report an empirical determination of the probability density functions P(r) of the number r of earthquakes in finite space-time windows for the California catalog, over fixed spatial boxes 5 x 5 km^2 and time intervals dt =1, 10, 100 and 1000 days
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We report on the existing connection between power-law distributions and allometries. As it was first reported in [PLoS ONE 7, e40393 (2012)] for the relationship between homicides and population, when these urban indicators present asymptotic power-
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