User:Schrew

Twitter Data Disaster Analysis

By utilizing linguistic tools and language processing, researchers can analyze Twitter data for research purposes. Twitter is a popular source for research because there are many daily users producing a constant stream of data. By using Twitter's API researchers can monitor this stream of data and come to conclusions. This data is especially valuable because it can be monitored in real time, or retroactively by building a timeline of past events. This ability allows researchers to track changes over time, an ability that is crucial to determining statistical relationships between factors. This was used in a number of studies, including: tracking social interactions regarding personal health^[1], predicting elections^[2], and predicting the rise and fall of the stock market^[3]. Twitter data has also been used to track floods, validate news stories during nuclear disaster events, and predict suicide ideation.

Geolocating Tweets After German Floods

Researchers tried to track the reach of natural disasters such as flooding by using Twitter’s API. The data collected was unstructured data from Twitter - specifically tweets that were georeferenced to the country of Germany. Features of these tweets include hashtags regarding flooding, as well as time stamps and location of these tweets. By clustering related tweets and plotting these over a map of Germany, we can determine the correlation between location and tweets regarding flooding. Also, we can compare word frequencies to see if there is a spike in disaster word usage in areas as they experience the disaster. The results concluded that there is no significant correlation between disaster word frequency and disaster-affected areas. The results also showed that it is too difficult to detect a disaster’s time and location based only on Twitter data, due to insufficient tools that can reliably relate tweets.^[4]

Predicting Suicidal Ideation

One of the risk factors of suicide is suicide ideation. Detecting suicide ideation can be helpful in suicide prevention. By using Reddit’s API, analyzing the text of Reddit users from Mental Health Subreddits, and by identifying which users also posted in /r/SuicideWatch, correlations between text and suicide ideation can be found. The users were classified into two groups, one group had Mental Health Subreddit posters who never posted in /r/SuicideWatch, the other group had users who posted in Mental Health Subreddits and then posted in /r/SuicideWatch within 6 months. The results showed that users who would go on to post in /r/SuicideWatch exhibited poorer linguistic structure and lower readability, higher self-attentional focus and greater detachment from the social realm, and lower social engagement and access to support. ^[5]

Credibility of Tweets Following Fukushima Disaster

Researchers focused on the earthquake and ensuing nuclear events in Fukushima Japan, in 2011. After an earthquake caused a tsunami that damaged nuclear units, a nuclear meltdown occurred, which leaked radiation into the surrounding areas. This study aimed to analyze the credibility of information sources on Twitter, as well as the influence of anonymity on credibility. Tweets were processed manually by researchers, pulling out pertinent data features such as: location, tweet type, credibility score, user type, and information source. The results show that most tweets following the Fukushima disaster were not original, and contained information from a source other than that tweet's owner. About 67.5% of information sources were found to be credible. If a user did not post a location along with the tweet, then it was statistically more likely to be false.^[6]

References

^ Teodoro, Rannie, and Mor Naaman. "Fitter with Twitter: Understanding Personal Health and Fitness Activity in Social Media." ICWSM. 2013.
^ Tumasjan, Andranik, et al. "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment." ICWSM 10 (2010): 178-185.
^ Bollen, Johan, Huina Mao, and Xiaojun Zeng. "Twitter mood predicts the stock market." Journal of Computational Science 2.1 (2011): 1-8.
^ Fuchs, Georg, et al. "Tracing the German centennial flood in the stream of tweets: first lessons learned." Proceedings of the second ACM SIGSPATIAL international workshop on crowdsourced and volunteered geographic information. ACM, 2013.
^ De Choudhury, Munmun, et al. "Discovering shifts to suicidal ideation from mental health content in social media." Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2016.
^ Thomson, Robert, et al. "Trusting tweets: The Fukushima disaster and information source credibility on Twitter." Proc. of ISCRAM 10 (2012).

[1] Teodoro, Rannie, and Mor Naaman. "Fitter with Twitter: Understanding Personal Health and Fitness Activity in Social Media." ICWSM. 2013.

[2] Tumasjan, Andranik, et al. "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment." ICWSM 10 (2010): 178-185.

[3] Bollen, Johan, Huina Mao, and Xiaojun Zeng. "Twitter mood predicts the stock market." Journal of Computational Science 2.1 (2011): 1-8.

[4] Fuchs, Georg, et al. "Tracing the German centennial flood in the stream of tweets: first lessons learned." Proceedings of the second ACM SIGSPATIAL international workshop on crowdsourced and volunteered geographic information. ACM, 2013.

[5] De Choudhury, Munmun, et al. "Discovering shifts to suicidal ideation from mental health content in social media." Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2016.

[6] Thomson, Robert, et al. "Trusting tweets: The Fukushima disaster and information source credibility on Twitter." Proc. of ISCRAM 10 (2012).

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