Final update before election: Net sentiment last 24 hours: Trump +7 ; Clinton -9. The last day analysis of social media. Buzz: So contrary to the popular belief, Trump actually is leading in social media just before the election day. Compare the above with last month ups and downs to put it in larger context: Last 3 month sentiment: Trump -11; Clinton -18. Buzz for Trump never fails: Trump's Word Clouds: Clinton's Word Clouds: Trump 3-month summary: Clinton 3-month summary: Ethnicity: RW: 伟哥的东西，好是好，就是没有体现美国的选人制度 Xin: 主要是白人黑人和亚裔人数比例并没有代表实际的选民百分比。 RW: 理论上讲，只要有一方得到所有选票的23%, 他或她就可能当选 【大数据跟踪美大选每日更新，希拉里成功反击，拉川普下水】 【社煤挖掘：大数据告诉我们，希拉里选情告急】 Trump sucks in social media big data in Spanish Did Trump’s Gettysburg speech enable the support rate to soar as claimed? 【社煤挖掘：川普的葛底斯堡演讲使支持率飙升了吗？】 【社煤挖掘：为什么要选ta而不是ta做总统？】 Big data mining shows clear social rating decline of Trump last month Clinton, 5 years ago. How time flies … 【社媒挖掘：川大叔喜大妈谁长出了总统样？】 【川普和希拉里的幽默竞赛】 【大数据舆情挖掘：希拉里川普最近一个月的形象消长】 欧阳峰： 论保守派该投票克林顿 【立委科普：自动民调】 【关于舆情挖掘】 《朝华午拾》总目录 【关于立委NLP的《关于系列》】 【置顶：立委NLP博文一览】 【 立委NLP频道 】
As promised, let us get down to the business of big data mining of public opinions and sentiments from Spanish social media on the US election campaign. We know that in the automated mining of public opinions and sentiments for Trump and Clinton we did before , Spanish-Americans are severely under-represented, with only 8% Hispanic posters in comparison with their 16% in population according to 2010 census (widely believed to be more than 16% today), perhaps because of language and/or cultural barriers. So we decide to use our multilingual mining tools to do a similar automated survey from Spanish Social Media to complement our earlier studies . This is Trump as represented in Spanish social media for the last 30 days (09/29-10/29), the key is his social rating as reflected by his net sentiment -33% (in comparison with his rating of -9% in English social media for the same period): way below the freezing point, it really sucks, as also illustrated by the concentration of negative Spanish expressions (red-font) in his word cloud visualization. By the net sentiment -33%, it corresponds to 242,672 negative mentions vs. 121,584 positive mentions, as shown below. In other words, negative comments are about twice as much as positive comments on Trump in Spanish social media in the last 30 days. This is the buzz in the last 30 days for Trump: mentions and potential impressions (eye balls): millions of data points and indeed a very hot topic in the social media. This is the BPI (Brand Passion Index) graph for directly comparing Trump and Clinton for their social ratings in the Spanish social media in the last 30 days: As seen, there is simply no comparison: to refresh our memory, let us contrast it with the BPI comparison in the English social media : Earlier in one of my election campaign mining posts on Chinese data , I said, if Chinese only were to vote, Trump would fail horribly, as shown by the big margin in the leading position of Clinton over Trump: This is even more true based on social media big data from Spanish. This is the comparison trends of passion intensity between Trump and Clinton: The visualization by weeks of the same passion intensity data, instead of by days, show even more clearly that people are very passionate about both candidates in the Spanish social media discussions, the intensity of sentiment expressed for Clinton are slightly higher than for Trump: This is the trends graph for their respective net sentiment, showing their social images in Spanish-speaking communities: We already know that there is simply no comparison: in this 30-day duration, even when Clinton dropped to its lowest point (close to zero) on Oct 9th, she was still way ahead of Trump whose net sentiment at the time was -40%. In any other time segments, we see an even bigger margin (as big as 40 to 80 points in gap) between the two. Clinton has consistently been leading. In terms of buzz, Trump generates more noise (mentions) than Clinton consistently, although the gap is not as large as that in English social media: This is the geo graph, so the social data come from mostly the US and Mexico, some from other Latin America countries and Spain: Since only the Mexicans in the US may have the voting power, we should exclude media from outside the US to have a clearer picture of how the Spanish-speaking voters may have an impact on this election. Before we do that filtering, we note the fact that Trump sucks in the minds of Mexican people, which is no surprise at all given his irresponsible comments about the Mexican people. Our social media tool is equipped with geo-filtering capabilities: you can add a geo-fence to a topic to retrieve all social media posts authored from within a fenced location. This allows you to analyze location-based content irrespective of post text. That is exactly what we need in order to do a study for Spanish-speaking communities in the US who are likely to be voters, excluding those media from Mexico or other Spanish-speaking countries. communities in the US who are likely to be voters, excluding those media from Mexico or other countries. This is also needed when we need to do study for those critical swing states to see the true pictures of the likelihood of the public sentiments and opinions in those states that will decide the destiny of the candidates and the future of the US (stay tuned, swing states social media mining will come shortly thanks to our fully automated mining system based on natural language deep parsing). Now I have excluded Spanish data from outside America, it turned out that the social ratings are roughly the same as before: the reduction of the data does not change the general public opinions from Spanish communities, US or beyond US., US or beyond US. This is US only Spanish social media: This is summary of Trump for Spanish data within US: It is clear that Trump's image truly sucks in the Spanish-speaking communities in the US, communities in the US, which is no surprise and so natural and evident that we simply just confirm and verify that with big data and high-tech now. These are sentiment drivers (i.e. pros and cons as well as emotion expressions) of Trump : We might need Google Translate to interpret them but the color coding remains universal: red is for negative comments and green is positive. More red than green means a poor image or social rating. In contrast, the Clinton's word clouds involve way more green than red: showing her support rate remains high in the Spanish-speaking communities of the US. It looks like that the emotional sentiments for Clinton are not as good as Clinton's sentiment drivers for her pros and cons. Sources of this study: Domains of this study: Did Trump's Gettysburg speech enable the support rate to soar as claimed? Big data mining shows clear social rating decline of Trump last month Clinton, 5 years ago. How time flies … Automated Suevey Dr Li’s NLP Blog in English
Last few days have seen tons of reports on Trump's Gettysburg speech and its impact on his support rate, which is claimed by some of his campaign media to soar due to this powerful speech. We would love to verify this and uncover the true picture based on big data mining from the social media. First, here is one link on his speech: DONALD J. TRUMP DELIVERS GROUNDBREAKING CONTRACT FOR THE AMERICAN VOTER IN GETTYSBURG . (The most widely circulated related post in Chinese social media seems to be this: Trump's heavyweight speech enables the soaring of the support rate and possible stock market crash ). Believed to be a historical speech in his last dash in the campaign, Trump basically said: I am willing to have a contract with the American people on reforming the politics and making America great again, with this plan outline of my administration in the time frame I promised when I am in office, I will make things happen, believe me. Trump made the speech on the 22nd this month, in order to mine true public opinions of the speech impact, we can investigate the data around 22nd for the social media automated data analysis. We believe that automated polling based on big data and language understanding technology is much more revealing and dependable than the traditional manual polls, with phone calls to something like 500 to 1,000 people. The latter is laughably lacking sufficient data to be trustworthy. What does the above trend graph tell us? 1 Trump in this time interval was indeed on the rise. The soaring claim this time does not entirely come out of nowhere, but, there is a big BUT. 2. BUT, a careful look at the public opinions represented by net sentiment (a measure reflecting the ratio of positive mentions over negative mentions in social media) shows that Trump has basically stayed below the freezing point (i.e. more negative than positive) in this time interval, with only a brief rise above the zero point near the 22nd speech, and soon went down underwater again. 3. The soaring claim cannot withstand scrutiny at all as soaring implies a sharp rise of support after the speech event in comparison with before, which is not the case. 4. The fact is, Uncle Trump's social media image dropped to the bottom on the 18th (with net sentiment of -20%) of this month. From 18th to 22nd when he delivered the speech, his net sentiment was steadily on rise from -20% to 0), but from 22nd to 25th, it no longer went up, but fell back down, so there is no ground for the claim of support soaring as an effect of his speech, not at all. 5. Although not soaring, Uncle Trump's speech did not lead to sharp drop either, in terms of the buzz generated, this speech can be said to be fairly well delivered in his performance. After the speech, the net sentiment of public opinions slightly dropped, basically maintaining the fundamentals close to zero. 6. The above big data investigation shows that the media campaign can be very misleading against the objective evidence and real life data. This is all propaganda, which cannot be trusted at its face value: from so-called support rate soared to possible stock market crash. Basically nonsense or noise of campaign, and it cannot be taken seriously. The following figure is a summary of the surveyed interval: As seen, the average public opinion net-sentiment for this interval is -9%, with positive rating consisting of 2.7 million mentions, and negative rating of 3.2 million mentions. How do we interpret -9% as an indicator of public opinions and sentiments? According to our previous numerous automated surveys of political figures, this is certainly not a good public opinion rating, but not particularly bad either as we have seen worse. Basically, -9% is under the average line among politicians reflecting the public image in people's minds in the social media. Nevertheless, compared with Trump's own public ratings before, there is a recorded 13 points jump in this interval, which is pretty good for him and his campaign. But the progress is clearly not the effect of his speech. This is the social media statistics on the data sources of this investigation: In terms of the ratio, Twitter ranks no 1, it is the most dynamic social media on politics for sure, with the largest amount of tweets generated every minute. Among a total of 34.5 million mentions on Trump, Twitter accounted for 23.9 million. In comparison, Facebook has 1.7 million mentions. Well, let's zoom in on the last 30 days instead of only the days around the speech, to provide a bigger background for uncovering the overall trends of this political fight in the 2016 US presidential campaign between Trump and Clinton. The 30 days range from 9/28-10/28, during which the two lines in the comparison trends chart show the contrast of Trump and Clinton in their respective daily ups and downs of net sentiment (reflecting their social rating trends). The general impression is that the fight seems to be fairly tight. Both are so scandal-ridden, both are tough and belligerent. And both are fairly poor in social ratings. The trends might look a bit clearer if we visualize the trends data by weeks instead of by day: No matter how much I dislike Trump, and regardless of my dislike of Clinton whom I have decided to vote anyway in order to make sure the annoying Trump is out of the race, as a data scientist , I have to rely on data which says that Hillary's recent situation is not too optimistic: Trump actually at times went a little ahead of Clinton (a troubling fact to recognize and see). The graph above shows a comparison of the mentions (buzz, so to speak). In terms of buzz, Trump is a natural topic-king, having generated most noise and comments, good or bad. Clinton is no comparison in this regard. The above is a comparison of public opinion passion intensity: like/love or dislike/hate? The passion intensity for Trump is really high, showing that he has some crazy fans and/or deep haters in the people. Hillary Clinton has been controversial also and it is not rare that we come across people with very intensified sentiments towards her too. But still, Trump is sort of political anomaly, and he is more likely to cause fanaticism or controversy than his opponent Hillary. In his recent Gettysburg speech, Trump highlighted the so-called danger of the election being manipulated. He clearly exaggerated the procedure risks, more than past candidates in history using the same election protocol and mechanism. By doing so, he paved the way for future non-recognition of the election results. He was even fooling the entire nation by saying publicly nonsense like he would totally accept the election results if he wins: this is not humor or sense of humor, it depicts a dangerous political figure with ambition unchecked. A very troubling sign and fairly dirty political tricks or fire he is playing with now, to my mind. Now the situation is, if Clinton has a substantial lead to beat him by a large margin, this old Uncle Trump would have no excuse or room for instigating incidents after the election. But if it is closer to see-saw, which is not unlikely given the trends analysis we have shown above, then our country might be in some trouble: Uncle Trump and his die-hard fans most certainly will make some trouble. Given the seriousness of this situation and pressing risks of political turmoil possibly to follow, we now see quite some people, including some conservative minds, begin to call for the election of Hillary for the sake of preventing Trump from possible trouble making. I am one with that mind-set too, given that I do not like Hillary either. If not for Trump, in ordinary elections like this when I do not like candidates of both major parties, I would most likely vote for a third party, or abstain from voting, but this election is different, it is too dangerous as it stands. It is like a time bomb hidden somewhere in the Trump's house, totally unpredictable. In order to prevent him from spilling, it is safer to vote for Clinton. in comparison with my earlier automated sentiment analysi blogged about a week ago ( Big data mining shows clear social rating decline of Trump last month )，this updated, more recent BPI brand comparison chart seems to be more see-saw: Clinton's recent campaign seems to be stuck somewhere. Over the last 30 days, Clinton's net sentiment rating is -17%, while Trump's is -19%. Clinton is only slightly ahead of Trump. Fortunately, Trump's speech did not really reverse the gap between the two, which is seen fairly clearly from the following historical trends represented by three different circles in brand comparison (the darker circle represents more recent data): the general trends of Clinton are still there: it started lagging behind and went better and now is a bit stuck, but still leading. Yes, Clinton's most recent campaign activities are not making significant progress, despite more resources put to use as shown by bigger darker circle in the graph. Among the three circles of Clinton, we can see that the smallest and lightest circle stands for the first 10 days of data in the past 30 days, starting obviously behind Trump. The last two circles are data of the last 20 days, seemingly in situ, although the circle becomes larger, indicating more campaign input and more buzz generated. But the benefits are not so obvious. On the other side, Trump's trends show a zigzag, with the overall trends actual declining in the past 30 days. The middle ten days, there was a clear rise in his social rating, but the last ten days have been going down back. Look at Trump's 30-day social cloud of Word Cloud for pros and cons and Word Cloud for emotions: Let us have a look at Trump's 30-day social media sentiment word clouds, the first is more about commenting on his pros and cons, and the second is more direct and emotional expressions on him: One friend took a glance at the red font expression fuck, and asked: what afre subjects and objects of fuck here?The subject generally does not appear, the default is a general network In fact, the subject generally does not appear in the social posts, by default it is the poster himself, reflecting part of the general public, the object of fuck is, of course, Trump, for otherwise our deep linguistics based system will not count it as a negative mention of trump reflected in the graph. Let us show some random samples side by side of the graph: My goodness, the fuck mentions accounted for 5% of the emotional data, the poor old Uncle Trump were fucked 40 million times in social media within only one-month duration, showing how this guy is hated by some of the people whom he is supposed to represent and govern if he takes office. See how they actually express their strong dislike of trump: fucking moron fucking idiot asshole shithead you name it, to the point even some Republicans also curse him like crazy: Trump is a fucking idiot. Thank you for ruining the Republican Party you shithead. Looking at the following figure of popular media, it seems that the most widely circulated political posts in social media involve quite some political video works: The domains figure below shows that the Tumblr posts on politics contribute more than Facebook: In terms of demographics background of social media posters, there is a fair balance between make and female: male 52% female 48% (in contrast to Chinese social media where only 25% females are posting political comments on US presidential campaign ). The figure below shows the ethnic background of the posters, with 70% Caucasians, 13% African Americans, 8% Hispanic and 6% Asians. It looks like that the Hispanic Americans and Asian Americans are under-represented in the English social media in comparison with their due population ratios, as a result, this study may have missed some of their voice (but we have another similar study using Chinese social media , which shows a clear and big lead of Clinton over Trump ; given time, we should do another automated survey using our multilingual engine for Spanish social media. Another suggestion from friends is to do a similar study on swing states because after all these are the key states that will decide the outcome of this election, we can filter the data by locations where posts are from to simulate that study). There might be a language or cultural reasons for this under-representation. This last table involves a bit of fun facts of the investigation. In social media, people tend to talk most about the campaign, on the Wednesday and Sunday evenings, with 9 o'clock as the peak, for example, on the topic of Trump, nine o'clock on Sunday evening generated 1,357,766 messages within one hour. No wonder there is no shortage of big data from social media on politics. It is all about big data. In contrast, with the traditional manual poll, no matter how sampling is done, the limitation in the number of data points is so challenging: with typically 500 to 1000 phone calls, how can we trust that the poll represents the public opinions of 200 million voters? They are laughably too sparse in data. Of course, in the pre-big-data age, there were simply no alternatives to collect public opinion in a timely manner with limited budgets. This is the beauty of Automatic Survey , which is bound to ourperform the manual survey and become the mainstream of polls. The following figure is the most influential followers authors: Authors with most followers are: Most mentioned authors are listed below： Tell me when in history did we ever have this much data and info, with this powerful data mining capabilities of fully sutomated mining of public opinions and sentiments at scale? Big data mining shows clear social rating decline of Trump last month Clinton, 5 years ago. How time flies … Automated Suevey Dr Li’s NLP Blog in English
Facebook, Twitter and YouTube Are Tools For Diplomacy by Jason Liebman (CEO and co-founder Howcast, co-founder of Alliance of Youth Movements) Posted: January 8, 2010 05:38 PM Last night I was honored to attend a small dinner with Secretary Hillary Clinton hosted at the U.S. State Department, which included a great list of notable guests, including Google CEO Eric Schmidt, Twitter Founder Jack Dorsey, Mobile Accord CEO James Eberhard, Microsoft CSO Craig Mundie, Cisco CMO Susan Bostron, NYU Professor Clay Shirky, and Personal Democracy founder Andrew Rasiej, to name a few. Secretary Clinton has a great deal of interest in technology and how it can drive engagement around the world. Kicking off 2010 with a two-hour discussion with technology leaders about 21st-century statecraft shows that she is serious about this. She realizes that if U.S. diplomatic policy is going to encourage civil society development, and fight violence and oppression, 21st-century tools like Twitter, Google, and YouTube are going to be key. It's all part of her 21st-century statecraft strategy: harnessing the power of technology tools to promote diplomacy around the globe. Yesterday, Senator Lugar wrote a great piece on these efforts. At dinner, we discussed how to harness technology for diplomatic and development goals. She went around the room asking everyone for concrete ideas. This brainstorming session with active participation from the group resulted in some great ideas, which included: 1. Finding ways to incent global citizens to build applications that can advance these goals 2. Finding creative ways to ensure that Internet access is always freely available 3. Building better public-private partnerships and making it easier for start-ups that have great ideas to be able to present them more effectively to the U.S. government 4. Ensuring we can better communicate leveraging language translation tools 5. Discovering ways to train people -- especially those who are new to the online world -- how to use all these tools effectively 6. Leveraging the mobile channel for anonymous crime reporting for greater transparency Jack Dorsey, James Eberhard, and I told her first hand about our experience on our recent tech delegation trips to Iraq and Mexico, which she was very excited about. We also spoke about the success and momentum of the Alliance of Youth Movements, a nonprofit organization I helped start which is looking to advance grassroots movements seeking positive social change using the tools of 21st-century technology. As a result of the dinner, Secretary Clinton declared in an e-mail to her staff: We are using all tools at our disposal to practice 21st-Century Statecraft...harnessing the power of technology. I was impressed with Secretary Clinton's engagement in the discussion and her genuine excitement to not only learn, but also make sure we act. From organizing a technology delegation to Iraq last April to sending Google CEO Eric Schmidt to Baghdad last month and hosting this dinner discussion, it's clear that the U.S. State Department is strongly supporting and encouraging digital diplomacy. By working with government and private-sector leaders, we're all banding together to figure out the most effective means to leverage digital technology tools to promote diplomacy around the world. 原文连接：http://www.huffingtonpost.com/jason-liebman/perspective-on-digital-di_b_416876.html
标签: Clinton
