Recent extreme events show that Twitter, a micro-blogging service, is emerging as the dominant social reporting tool to spread information on social crises. It is elevating the online public community to the status of first responders who can collectively cope with social crises. However, at the same time, many warnings have been raised about the reliability of community intelligence obtained through social reporting by the amateur online community. Using rumor theory, this paper studies citizen-driven information processing through Twitter services using data from three social crises: the Mumbai terrorist attacks in 2008, the Toyota recall in 2010, and the Seattle café shooting incident in 2012. We approach social crises as communal efforts for community intelligence gathering and collective information processing to cope with and adapt to uncertain external situations. We explore two issues: (1) collective social reporting as an information processing mechanism to address crisis problems and gather community intelligence, and (2) the degeneration of social reporting into collective rumor mills. Our analysis reveals that information with no clear source provided was the most important, personal involvement next in importance, and anxiety the least yet still important rumor causing factor on Twitter under social crisis situations.

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Abstract-The Capability Maturity Model (CMM) has become a popular methodology for improving software development processes with the goal of developing high-quality software within budget and planned cycle time. Prior research literature, while not exclusively focusing on CMM level 5 projects, has identified a host of factors as determinants of software development effort, quality, and cycle time. In this study, we focus exclusively on CMM level 5 projects from multiple organizations to study the impacts of highly mature processes on effort, quality, and cycle time. Using a linear regression model based on data collected from 37 CMM level 5 projects of four organizations, we find that high levels of process maturity, as indicated by CMM level 5 rating, reduce the effects of most factors that were previously believed to impact software development effort, quality, and cycle time. The only factor found to be significant in determining effort, cycle time, and quality was software size. On the average, the developed models predicted effort and cycle time around 12 percent and defects to about 49 percent of the actuals, across organizations. Overall, the results in this paper indicate that some of the biggest rewards from high levels of process maturity come from the reduction in variance of software development outcomes that were caused by factors other than software size.

This paper analyzes the role of situational information as an antecedent of terrorists' opportunistic decision making in the volatile and extreme environment of the Mumbai terrorist attack. We especially focus on how Mumbai terrorists monitored and utilized situational information to mount attacks against civilians. Situational information which was broadcast through live media and Twitter contributed to the terrorists' decision making process and, as a result, increased the effectiveness of hand-held weapons to accomplish their terrorist goal. By utilizing a framework drawn from Situation Awareness (SA) theory, this paper aims to (1) analyze the content of Twitter postings of the Mumbai terror incident, (2) expose the vulnerabilities of Twitter as a participatory emergency reporting system in the terrorism context, and (3), based on the content analysis of Twitter postings, we suggest a conceptual framework for analyzing information control in the context of terrorism.

Social Network Services (SNS) such as Twitter play a significant role in reporting media, particularly during the extreme events. We examined the impact of tweet features on the diffusion of two types of messages during 2013 Boston marathon tragedy-rumor related and non-rumor related (both in the context of the Boston tragedy). Negative binomial analysis revealed that tweet features such as reaction time, number of followers, and usage of hashtag have an impact on tweet message diffusion during the tragedy. The number of followers showed a positive relationship with message diffusion. However, the relationship between tweet reaction time and message diffusion was negative. Finally, tweet messages that did not include hashtags diffused more than messages that contained hashtags. This paper contributes by adapting the innovation diffusion model to explore tweet message diffusion in Twitter space during extreme events.