OSoMe: the IUNI observatory on social media

Abstract

The study of social phenomena is becoming increasingly reliant on big data from online social networks. Broad access to social media data, however, requires software development skills that not all researchers possess. Here we present the IUNI Observatory on Social Media, an open analytics platform designed to facilitate computational social science. The system leverages a historical, ongoing collection of over 70 billion public messages from Twitter. We illustrate a number of interactive open-source tools to retrieve, visualize, and analyze derived data from this collection. The Observatory,

Contagion dynamics of extremist propaganda in social networks

“…The next step of our study consisted in collecting data related to the activity of the 25,538 ISIS supporters on Twitter. To this purpose, we leveraged the Observatory on Social Media (OSoMe) data source set up by our collaborators at Indiana University [20], which continuously collects the Twitter data stream from the gardenhose API (roughly a 10% random sample of the full Twitter data stream). Using this large data stream avoids known issues derived by using the public Twitter stream API which serves only less than 1% of the overall tweets [47].…”

“…• Use large social media data streams, when available, rather than small samples that can be biased. Services like the Indiana University's OSoMe database [20] can provide data sources especially valuable for Twitter-based social media studies. Alternatively, the Twitter Search API can yield comprehensive data around specific users or topics, provided that the search is limited to short time frames.…”

Predictive Analysis on Twitter: Techniques and Applications

“…Gaining insights and improving situational awareness on issues that matter to the public are challenging tasks, and social media can be harnessed for a better understanding of the pulse of the populace. Accordingly, state-of-theart applications, such as Twitris [3] and OSoMe [2], have been developed to process and analyze big social media data in real time. Regarding availability and popularity, Twitter data is more common than data from web forums and Reddit 1 .…”

Fighting fake news: a role for computational social science in the fight against digital misinformation

“…These include: news gathering, verification, and delivery of corrections [42][43][44][45][46]. These activities are already capitalizing on the growing number of tools, data sets, and platforms contributed by computer scientists to detect, define, model, and counteract the spread of misinformation [47][48][49][50][51][52][53][54][55][56]. Without a clear understanding of what are the most effective countermeasures, and of who is best equipped to deliver them, these tools may never be brought to complete fruition.…”