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Online social network services, such as Facebook and Twitter, have become increasingly popular recently. More and more users are accustomed to regularly reading the latest news feeds and interacting with friends on these social websites. However, when the number of friends increases to a large extent, users will receive hundreds of messages in a day and may be overwhelmed by the information overload. To alleviate this problem, we propose a novel visualization technique for social news feeds summarization on social web services. The proposed system SocFeedViewer can produce an egocentric network graph based on the news feeds generated in an arbitrary period of time. This graph provides an overview of those who have generated news feeds during this time period. To enhance the reading experience, we incorporate community detection, connectivity analysis, and importance analysis into our system to make users capable of preferentially surfing news feeds that are more significant and interesting.