In previous work we reported that resources linked in tweets disappeared at the rate of 11 in the first year followed by 7.3 each year afterwards. We also found that in the first year 6.7 , and 14.6 in each subsequent year, of the resources were archived in public web archives. In this paper we revisit the same dataset of tweets and find that our prior model still holds and the calculated error for estimating percentages missing was about 4 , but we found the rate of archiving produced a higher error of about 11.5 . We also discovered that resources have disappeared from the archives themselves (7.89 ) as well as reappeared on the live web after being declared missing (6.54 ). We have also tested the availability of the tweets themselves and found that 10.34 have disappeared from the live web. To mitigate the loss of resources on the live web, we propose the use of a "tweet signature". Using the Topsy API, we extract the top five most frequent terms from the union of all tweets about a resource, and use these five terms as a query to Google. We found that using tweet signatures results in discovering replacement resources with 70+ textual similarity to the missing resource 41 of the time.
Along with understanding the nature of the social media, researchers analyzed user behavior on the social networks in general. By analyzing user activity click logs, aimed to get a better understanding of social interactions social browsing patterns @cite_13 . Zhao and Rosson aimed to explore the reasons of how and why people use Twitter and this use's impact on informal communication at work @cite_16 . Following the how and the why, attempted to answer the next question of what is the user-generated content is about by investigating personal weblogs to detect the effects of personality, topic type, and the general motivation in published blogs @cite_19 . Yang and Counts investigated the information diffusion speed, scale and range in Twitter and how they could be predicted @cite_1 .
@cite_1 @cite_19 @cite_16 @cite_13
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Twitter has become a significant means by which people communicate with the world and describe their current activities, opinions and status in short text snippets. Tweets can be analyzed automatically in order to derive much potential information such as, interesting topics, social influence, user’s communities, etc. Community extraction within social networks has been a focus of recent work in several areas. Different from the most community discovery methods focused on the relations between users, we aim to derive user’s communities based on common topics from user’s tweets. For instance, if two users always talk about politic in their tweets, thus they can be grouped in the same community which is related to politic topic. To achieve this goal, we propose a new approach called CETD: Community Extraction based on Topic-Driven-Model. This approach combines our proposed model used to detect topics of the user’s tweets based on a semantic taxonomy together with a community extraction method based on the hierarchical clustering technique. Our experimentation on the proposed approach shows the relevant of the users communities extracted based on their common topics and domains. Most previous analysis of Twitter user behavior has focused on individual information cascades and the social followers graph, in which the nodes for two users are connected if one follows the other. We instead study aggregate user behavior and the retweet graph with a focus on quantitative descriptions. We find that the lifetime tweet distribution is a type-II discrete Weibull stemming from a power law hazard function, that the tweet rate distribution, although asymptotically power law, exhibits a lognormal cutoff over finite sample intervals, and that the inter-tweet interval distribution is a power law with exponential cutoff. The retweet graph is small-world and scale-free, like the social graph, but less disassortative and has much stronger clustering. These differences are consistent with it better capturing the real-world social relationships of and trust between users than the social graph. Beyond just understanding and modeling human communication patterns and social networks, applications for alternative, decentralized microblogging systems---both predicting real-word performance and detecting spam---are discussed. In this article, we analyze and compare user behavior on two different microblogging platforms: (1) Sina Weibo which is the most popular microblogging service in China and (2) Twitter. Such a comparison has not been done before at this scale and is therefore essential for understanding user behavior on microblogging services. In our study, we analyze more than 40 million microblogging activities and investigate microblogging behavior from different angles. We (i) analyze how people access microblogs and (ii) compare the writing style of Sina Weibo and Twitter users by analyzing textual features of microposts. Based on semantics and sentiments that our user modeling framework extracts from English and Chinese posts, we study and compare (iii) the topics and (iv) sentiment polarities of posts on Sina Weibo and Twitter. Furthermore, (v) we investigate the temporal dynamics of the microblogging behavior such as the drift of user interests over time. Our results reveal significant differences in the microblogging behavior on Sina Weibo and Twitter and deliver valuable insights for multilingual and culture-aware user modeling based on microblogging data. We also explore the correlation between some of these differences and cultural models from social science research. Previous studies have established the link between one's actions (e.g., engaging with others vs. minding one's own business) and one's national culture (e.g., collectivist vs. individualistic), and such actions have been shown to be important as they are collectively affiliated with a country's economic outcomes (e.g., Gross Domestic Product). Hitherto there has not been any systematic study of whether one's action on Twitter (e.g., deciding when to post messages) is linked to one's culture (e.g., country's Pace of Life). To fix that, we build different network snapshots starting from 55,000 seed users on Twitter, and we do so for 10 weeks across 30 countries (after filtering those with low penetration rates) for a total of 2.34 M profiles. Based on Hofstede's theory of cultural dimensions and Levine's Pace of Life theory, we consider three behavioral patterns on Twitter (i.e., temporal predictability of tweets, engaging with others, and supporting others who are less popular) and associate them with three different dimensions derived from the two theories: Pace of Life, Individualism and Power Distance. We find the following strong correlations: activity predictability negatively correlates with Pace of Life (r=-0.62), tweets with mentions negatively correlates with Individualism (r = -0.55), and power (e.g, Twitter popularity) imbalance in relationships (between, for example, two users mentioning each other) is correlated with Power Distance (r=0.62). These three cultural dimensions matter because they are associated with a country's socio-economic aspects - with GDP per capita, income inequality, and education expenditure.