| 1. |
- Holmstrom, Jesper, et al.
(författare)
-
Do we Read what we Share? Analyzing the Click Dynamic of News Articles Shared on Twitter
- 2019
-
Ingår i: PROCEEDINGS OF THE 2019 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2019). - New York, NY, USA : Institute of Electrical and Electronics Engineers (IEEE). - 9781450368681 ; , s. 420-425
-
Konferensbidrag (refereegranskat)abstract
- News and information spread over social media can have big impact on thoughts, beliefs, and opinions. It is therefore important to understand the sharing dynamics on these forums. However, most studies trying to capture these dynamics rely only on Twitters open APIs to measure how frequently articles are shared/retweeted, and therefore do not capture how many users actually read the articles linked in these tweets. To address this problem, in this paper, we first develop a novel measurement methodology, which combines the Twitter steaming API, the Bitly API, and careful sample rate selection to simultaneously collect and analyze the timeline of both the number of retweets and clicks generated by news article links. Second, we present a temporal analysis of the news cycle based on five-day-long traces (containing both clicks and retweet over time) for the news article links discovered during a seven-day period. Among other things, our analysis highlights differences in the relative timelines observed for clicks and retweets (e.g., retweet data often lags and underestimates the bias towards reading popular links/articles), and helps answer important questions regarding differences in how age-based biases and churn affect how frequently news articles shared on Twitter are accessed over time.
|
|
| 2. |
- Linder, Tova, et al.
(författare)
-
On Using Crowd-sourced Network Measurements for Performance Prediction
- 2016
-
Ingår i: <em>Proc. IEEE/IFIP Wireless On-demand Network Systems and Services Conference (IEEE/IFIP WONS)</em>, Cortina d'Ampezzo, Italy, Jan. 2016.. - : IEEE Computer Society Digital Library. - 9783901882791 ; , s. 1-8
-
Konferensbidrag (refereegranskat)abstract
- Geo-location-based bandwidth prediction together with careful download scheduling for mobile clients can be used to minimize download times, reduce energy usage, and improve streaming performance. Although crowd-sourced measurements provide an important prediction tool, little is known about the prediction accuracy and improvements such datasets can provide. In this paper we use a large-scale crowd-sourced dataset from Bredbandskollen, Sweden's primary speedtest service, to evaluate the prediction accuracy and achievable performance improvements with such data. We first present a scalable performance map methodology that allows fast insertion/retrieval of geo-sparse measurements, and use this methodology to characterize the Bredbandskollen usage. Second, we analyze the bandwidth variations and predictability of the download speeds observed within and across different locations, when accounting for various factors. Third, we evaluate the relative performance improvements achievable by users leveraging different subsets of measurements (capturing effects of limited sharing or filtering based on operator, network technology, or both) when predicting opportune locations to perform downloads. Our results are encouraging for both centralized and peer-to-peer performance map solutions. For example, most measurements are done in locations with many measurements and good prediction accuracy, and further improvements are possible through filtering (e.g., based on operator and technology) or limited information sharing.
|
|
