Sentiment score of relevant posts identified from social media by date.

<p>Relevant posts mentioned impact of unplanned school closure due to Chicago teachers’ strike from September 8–21 (two days before to three days after strike) on students and their families (N = 930). Sentiment score was calculated as: (positive posts—negative posts)/(positive posts + negative posts + neutral posts)^a-b. ^aSentiment score < 0 suggests negative sentiment; score > 0 suggests positive sentiment. ^bScore on September 21 reflects only four relevant posts, all expressing negative sentiment.</p

Distribution of relevant and irrelevant posts by social media type.

<p>Relevant posts further categorized according negative, positive, and neutral sentiment. Posts captured from social media referencing Chicago teachers’ strike from September 8–21, 2012 (two days before to three days after strike).</p

Number of relevant posts identified from social media by sentiment^a and date.

<p>Relevant posts mentioned impact of unplanned school closure due to Chicago teachers’ strike from September 8–21, 2012 (two days before and three days after strike) on students and their families (n = 930). ^aSentiment definitions: Positive: The author expressed a good or favorable experience as a result of the closure. Example of positive post: “Another day without school, a day to play.” Negative: The author expressed inconveniences or undesirable effect as a result of the closure. Example of negative post: “I can’t find childcare”. Neutral: The author did not express any particular sentiment. Example of neutral post: “Schools will be open at 8:00 to serve breakfast to students”.</p

Online work force analyzes social media to identify consequences of an unplanned school closure – using technology to prepare for the next pandemic

We used the social media-monitoring platform Radian6 (San Francisco, CA) to retrospectively capture social media posts related to the Chicago City School District closure in September 2012. Social media in dataset include posts from Twitter, Facebook, blogs, forums, and comments between September 8 and September 12, (two days before the strike started to two days after the strike ended). We used the following combination of search terms: “strike Chicago” AND “breakfast” OR “childcare” OR “daycare” OR “lunch” OR “parent”.  A proximity score of “5” was applied to the terms “strike” and “Chicago” (on a scale of 1–20, with 1 being exact [i.e., strike and Chicago together]).<div><br></div><div>Column headings include:  Unique post identifying number (NUMBER), post content (CONTENT), social media provider (MEDIA_PROVIDER), and publishing date/time (PUBLISH_DATE).  </div><div><br></div><div>These posts were reviewed and categorized as relevant (related to impact of closure on students and their families) or irrelevant (describing political aspects of strike, welfare system, or other unrelated topics).  Relevant posts were further analyzed for underlying sentiment (positive, neutral, or negative).</div

Boletín de Segovia: Número 53 - 1834 julio 7

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Qualitative comparison of the Chicago teacher’s strike social media findings with results from traditional household surveys in Mississippi, Colorado, and Kentucky and a telephone poll about the costs and consequences of unplanned school closures^a.

<p>Qualitative comparison of the Chicago teacher’s strike social media findings with results from traditional household surveys in Mississippi, Colorado, and Kentucky and a telephone poll about the costs and consequences of unplanned school closures<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163207#t003fn001" target="_blank">^a</a>.</p

Week 1 ILI rate prediction for HHS regions 6–10 from influenza seasons 2004–2005 to 2012–2013.

<p>Note: 1) Auto-regressive integrated moving average (ARIMA [<i>p</i>, <i>d</i>, <i>q</i>]) method, in which <i>p</i> represents the number of auto-regressive terms, <i>d</i> is the number of non-seasonal differences and <i>q</i> is the number of lagged forecast errors in the prediction equation; 2) 95% prediction intervals were calculated by bootstrapping the model error 5,000 times; and 3) the point estimate and prediction interval were bolded if the observed ILIs rate were not covered by the 95% prediction interval.</p><p>Week 1 ILI rate prediction for HHS regions 6–10 from influenza seasons 2004–2005 to 2012–2013.</p

Last Week ILI rate prediction for HHS regions 1–5 from influenza seasons 2004–2005 to 2012–2013.

<p>Last Week ILI rate prediction for HHS regions 1–5 from influenza seasons 2004–2005 to 2012–2013.</p

a) Number of predicted that are significantly lower(the upper bound of 95% prediction interval lower than the observed) than observed across influenza season and weeks, b) Number of predicted that are significantly higher(the lower bound of 95% prediction interval higher than the observed) than observed across influenza season and weeks.

<p>a) Number of predicted that are significantly lower(the upper bound of 95% prediction interval lower than the observed) than observed across influenza season and weeks, b) Number of predicted that are significantly higher(the lower bound of 95% prediction interval higher than the observed) than observed across influenza season and weeks.</p

Average total number of patient visits (red line, scales on the left-side y-axis) and average total number of ILI visits (blue line, scales on the right-side y-axis) across all HHS regions between 2003–2004 and 2012–2013 influenza seasons.

<p>Average total number of patient visits (red line, scales on the left-side y-axis) and average total number of ILI visits (blue line, scales on the right-side y-axis) across all HHS regions between 2003–2004 and 2012–2013 influenza seasons.</p