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In the world of Facebook, Twitter and Yelp, water-cooler conversations with co-workers and backyard small talk with neighbors have moved from the physical world to the digital arena. In this new landscape, organizations ranging from Fortune 500 companies to government agencies to political campaigns continuously monitor online opinions in an effort to guide their actions. Are consumers satisfied with our product? How are our policies perceived? Do voters agree with our platform? Measuring online opinion is more complex than just reading a few posted reviews. Social media is replete with noise and chatter that can contaminate monitoring efforts. By knowing what shapes online opinions, organizations can better uncover the valuable insights hidden in the social media chatter and better inform strategy. This book can help anyone facing the challenge of making sense of social media data to move beyond the current practice of social media monitoring to more comprehensive use of social media intelligence

Abstract
Social TV is the simultaneous consumption of television alongside social media chatter about the programming. This topic is highly relevant for marketers. Usually it is considered as a bad thing for TV advertisers. While there can be distraction from the ads, marketers can also benefit from positive effects. Consumers' multiscreen activities can be used to attract more viewers, to leverage TV campaigns and to increase sales. This chatter creates free exposure for the brand online, extends the reach of television ad campaigns to the online space, and offers real-time feedback to advertisers on how their ads are being received. To take advantage of social TV, marketers need to develop a social media and ad design strategy for TV shows. Not every "social show" is good for them. Many programs receive a high volume of program-related chatter at the expense of advertiser-related word-of-mouth, but some programs generate high levels of online conversations that can also benefit their advertisers. Marketers are well served to identify those programs that are conducive to advertiser-related chatter. Also, specific ad designs can further encourage buzz.

Abstract
Online consumer reviews are a convenient, valuable pool of information for potential buyers as well as for marketers. But how reliable is this information? A closer look at the motives behind posted product reviews shows that online opinions should indeed be interpreted with care. The study results reveal that the composition of the customer base can exert a substantial influence on the posted online opinion. Due to selection bias and adjustment effects over time, the content posted may not necessarily reflect the customer base's overall opinion of the product. Individuals were more likely to submit ratings when they were either very satisfied or not satisfied. In addition, consumers were more likely to post an opinion when the ratings already posted were more positive. Highly engaged consumers who frequently post their opinions tend to be more negative than less-engaged individuals. Even though decision-makers can gain valuable insights from the analysis of this pool of information, they should not rely on this source exclusively.

Abstract
This empirical study investigates whether unpleasant weather—a prominent aspect of a consumer's offline environment—influences online review provision and content. It uses a unique dataset that combines 12 years of data on hotel bookings and reviews, with weather condition information at a consumer's home and hotel address. The results show that bad weather increases review provision and reduces rating scores for past consumption experiences. Moreover, 6.5% more reviews are written on rainy days and that these reviews are 0.1 points lower, accounting for 59% of the difference in average rating scores between four- and five-star hotels in our data. These results are consistent with a scenario in which bad weather (i) induces negative consumer mood, lowering rating scores, and (ii) makes consumers less time-constrained, which increases review provision. Additional analyses with various automated sentiment measures for almost 300,000 review texts support this scenario: reviews on rainy days show a significant reduction in reviewer positivity and happiness, yet are longer and more detailed. This study demonstrates that offline context influences online reviews, and discusses how platforms and businesses should include contextual information in their review management approaches.