( CNN ) -- How are fads started and spread ? Do certain influential people play a key role , or is it truly random ? How does a trend go from new and exciting to old and passe so quickly ? Does having happy friends have an effect on our own happiness ? Maybe Twitter can tell us . Every second , millions of people across the world are sharing their thoughts in the form of 140-character messages using Twitter . The `` tweets '' range from the mundane to the profound , and convey , for example , what people are doing , thinking and reading at any moment . The amount of information in any individual tweet is highly variable , but in aggregate the more than 65 million tweets composed per day represent a detailed , real-time trace of the collective thoughts and feelings of a significant fraction of the population -- potentially offering valuable information to everyone from politicians to advertisers to social researchers . To demonstrate the unique power of Twitter data , our research group at Northeastern University and Harvard Medical School recently began a study to infer the mood of Twitter users in the United States from their public tweets . We can observe very distinct patterns over the course of the day , as well as weekly patterns that match conventional wisdom , such as the tendency of users to write happier tweets on weekends . We also observe geographic variations , with users from Hawaii , California , and Florida , for example , using happier words in their tweets . With Twitter , we now not only know which users are communicating , but we also know what they are saying . From a research perspective , Twitter is more than just a new tool ; it 's an entirely new kind of tool . Never before have academic researchers had access to this much real-time public information about what people are thinking and saying . It is analogous to being allowed to tap into millions of water-cooler conversations , school rooms and other public conversations across the globe . Our study is preliminary ; we need more data to do a proper evaluation , and the results are subject to any number of biases ( people using language differently across the United States , and different demographics using Twitter at different times ) . Our approach simply looks , for example , for occurrences of `` happy '' or `` unhappy '' words in tweets . However , because we take words out of context , our approach will not correctly interpret tweets like `` I am not happy '' . Even so , initial results demonstrate that Twitter data contain a wealth of information , and that even relatively simplistic approaches such as ours can extract interesting results . In fact , other research groups have also begun to examine Twitter data and have demonstrated that it can be used to predict the box-office success of an upcoming movie . And Twitter data yields much more detailed polling when compared to traditional methods , enabling real-time feedback for issues that are of local , national or international interest . In the past , researchers studying traces of human communication , such as phone records , have shown that the social network that connects us has rich hidden complexity . But legal and privacy concerns have caused these previous studies to almost universally omit the content of the communication . Because most users leave their tweets public , Twitter represents an unprecedented opportunity . This is why researchers owe a debt of gratitude to Twitter for its policy of open access to public tweets ( exemplified by the recent donation of its entire public tweet history to the Library of Congress ) . Like any scientific tool , the ability to use the data for research is subject to caveats and limitations . Unlike many existing tools , such as surveys and polls , researchers can not ask a question of the Twitter users directly ; instead , researchers must determine whether the question is one that the Twitter data can answer . Should we determine how to extract information reliably , which we 're working on , the potential applications of the data are almost endless . For example , monitoring the mood of the public chatter on Twitter could allow businesses to quickly identify and respond to incidents , mitigating the effect of negative publicity on their brand . The data could be used to inform public policy , allowing public officials and politicians to receive feedback from their constituents in real time . From a scientific standpoint , Twitter data can shed light on how information spreads through society . Researchers can also investigate network effects : How does what our friends discuss influence what we discuss ? In short , the data that is now becoming available from Twitter and related websites offers a new lens through which we can view society . It promises new approaches to understanding social phenomena , what some colleagues have dubbed `` computational social science . '' This new kind of data presents new challenges , such as privacy , anonymity and legality , but developing a science around it has the potential to do enormous good . The opinions expressed in this commentary are solely those of Alan Mislove .
Mislove : We trace the thoughts of a significant fraction of the population . He 's researching profiles of all Twitter users based on their thoughts . Research looks , for example , for occurrences of ` ` happy '' or ` ` unhappy '' thoughts in tweets .