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Economic Issues

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International Issues

  • Foreign Policy
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Social Issues

  • Abortion
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Popular Topics
Click on a graph point to see the popular topics for that day.
Tweets for Topics
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Active Hash Tags (past 2 hours)
Active Users (past 2 hours)
[CLICK ON A PUSHPIN TO SEE POPULAR TOPICS FROM A LOCATION, ON CHOSEN DATE.]
This location analysis is based only on tweets which have location metadata (~10% of total volume). Be sure to select both time and region of interest to see spatio-temporal-thematic social signals.
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The tag cloud starts by summarizing latest tweets for the event and evolves as new tweets arrive. Only those tweets with location information are shown on the map.

Note: This map and tag cloud are updated to the second based on the 2012 U.S. Presidential Election activity on Twitter.

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The following list shows top 100 influential Twitter users while talking about a topic, which can be multi-faceted - thematic,person,organization etc. The following network shows the connectivity among these influential Twitter users about a topic chosen on left menu. Colors denote the user level characteristics such as sentiment polarity for the topic, political affiliation, profession etc. Here, we specifically decided to show the characteristics and connectivity of top users, because they have potential to drive the community for desired actions. (Please check our Insights tab for such examples) Wondering about Science behind it? Check here.

== Description:==
Nov. 4, 2012.

The following interaction networks of influential users talking about presidential candidates Romney vs. Obama, show the user's sentiment for the target candidate (e.g., Positive attribute in the Obama's network shows positive leaning of the influencer to Obama side). It is interesting to see the structure of these top 100 influencers in the two communities:

1.) Obama's community appears to have increased cohesiveness after the final debate performance and the Hurricane Sandy Storm, now even more strongly cohesive compared to Romney's community. There is substantial gain on the state after the final debate. (Please check out 'Insights' tab for the evolution)
2.) Romney's community appears really weak after the storm, can it be due to his negative comments about FEMA's performance and president's role as command-in-chief, though most of the efforts went on to do as best as they could for the emergency response! Interestingly, there are increasing no. of negative influencers in both the communities as well. But the gain in the positive influencers' cohesiveness in the Obama community definitely overwhelms this slight increase, partly due to his superior performance in the last two debates and also acting as the true leader, 'command-in-chief' during national disaster event.
(Click on a user in the list or node in the network to see user profile)

Emerging Community Leaders to engage with
User Interaction Network of emerging leaders

Barack Obama

Mitt Romney

User Attribute
  • Pos
  • Neg
  • Objective

Deeper Insights on Electorate Perceptions: Connecting Candidates-Events-Topics Using Sentiments and Emotions

Can #socialmedia give deeper insights into #election2012 based on #sentiment  #emotion and #popularity analysis/cues?

How do events (e.g., debates, Bengazi attack) unfold through sentiments, emotions and popularity associated with candidates,  and what thematic insights (reasons) do they reveal?

Below is Dr. Amit Sheth's Facebook post at 5:45pm November 6th 2012 (closing date), making a prediction based on twitris analysis results.

Below are just some of the examples of unusual insights associated with Sentiment, Emotion and Popularity that you will find at Twitris live: http://analysis.knoesis.org/uselection/sentiment/  . [Note: use date selection field to go back in time.]

Send us your questions/feedback at amit@knoesis.org  

Sentiment Analysis on Election Day

The following screenshots were taken on Nov. 6th 6pm. Twitris shows that Obama leads the race in Colorado, Florida, Iowa and Ohio, while Obama/Romney race is very close in Virginia.

Sentiment Analysis on Presidential Debate

Obama

  1. After the first debate, Twitris shows substantial decrease of positive sentiment for Obama on Oct. 4; it also shows the negative topics that related to such decrease such as last night, first debate, debate performance, debate presidencial, etc.

  1. On Oct. 5, the positive sentiment for Obama went up to the previous level, and Twitris captured the topics that may cause such increase as obama fights back after debate, jobs report, good news.

  1. Three days before the second presidential debate (Oct. 16th), Twitris observes an increase in positive sentiment for Obama. Twitris shows the positive topics that related to such increase include: next debate, bruce springsteen, wonderful world for children, Barack Obama, president U.S.A. etc.

           

  1. Tweets such as “Please make my dream - the wonderful world for children - real!The next step is -Barack Obama president USA in the future!!!”, “My dream is : The first child is the future for next child! The most important step is : Barack Obama president U.S.A. forever” have been retweeted a lot.

  1. A sharp decrease in the positive sentiment for Obama is observed on Oct. 16, and Twitris shows the most popular topics related to such decrease include 25trends Elections 2012 analyses, Mariah Carey, Nicki Minaj.

  1. Twitris shows a slight decrease followed by an increase in positive sentiment for Obama after the second presidential debate. It also shows that the positive topics for Obama include debate last night, honey boo boo, last nights debate, bruce springsteen, salt lake tribune, etc; and negative topics for Obama include luntz focus group, candy crowley, tagg romney, 25trends elections 2012 analyses, foreign policy, campaign accepted foreign web donation, etc.

Romney

  1. After the first debate, Twitris shows a substantial decrease of positive sentiment for Romney on Oct. 4; it shows positive topics such as romney won the debate, us election, debate with obama, ann romney, but seems the negative topics such as big bird and sesame street are more dominated.

  1. From Oct. 5 to Oct. 12, positive sentiment for Romney increases steadily, and Twitris shows the relevant positive topics such as romney surges out of debate, debate performance, only choice, gallup poll, swing states, lead over obama, etc. Though it also shows many negative topics, Romney’s win of the first debate is obviously more influential.

  1. Three days before the second presidential debate, Twitris obverses a decrease (Oct. 14th) and followed by an increase (Oct. 15th) in positive sentiment for Romney.

Twitris shows the negative topics related to the decrease on Oct. 14th including:

supporter wears shocking racist t-shirt, romney becomes president,

sensata workers are living proof, hits back hard at romney, etc.

And positive topics related to the increase on Oct. 15 such as whose website is

            faster, mitt romney 's website, more than two seconds.

  1. A sharp decrease in the positive sentiment for Romney is observed on Oct. 16, and Twitris shows the most popular topics related to such a decrease, including tax plan, tax cut and president clinton.
  2. Twitris shows a decrease in positive sentiment of Romney on Oct. 17, and the negative topics such as presidential debate, last night, and second debate suggest that he lost the second debate, and other negative topics include binders full of women and tax plan.

  1. An increase in positive sentiment of Romney can be observed after Oct. 17. Twitris captured the positive topics that may contribute to such an increase include 25trends Elections 2012 analyses, likely voters, orlando sentinel, lee iacocca endorses romney, etc. It also shows negative topics of Romney such as tagg romney, salt lake, foreign policy, etc.

Emotion

Emotion Joy:

Twitter users got excited before both debates on Oct. 3rd and Oct. 16th. For example, “1st debate tonight, Obama v #Romneyshambles should be fun, Looking forward to the 2nd presidential debate tonight . . . unless Obama pulls another Norv.”

Twitter users were also very excited when Obama went to daily show. For example, “PRESIDENT OBAMA WILL BE ON THE DAILY SHOW TONIGHT AT 11PM EVERYBODY WATCH!

Emotion Anger:

Twitter users were angry about Romney on Sep. 24, 25 and 26, because of the “airplane windows” incident. I.e., “Mitt Romney literally said he doesn't understand why airplane windows don't open? And we're still letting him run for president?”

Twitter users were angry about Romney on Oct. 5, 6 and 7, because of the “big bird” incident. I.e., “Mitt Romney's Pathetic. How Can He Cancel Big bird ? Like wtf. Sit Your Ass White Ass Down.”

Emotion Fear:

There is a pike on emotion fear on Sep. 26th. And the reason is that the US embassy in Libya was attacked on that day.

Popularity

  1. After the second presidential debate, Obama’s popularity/social media presence (number of tweets) has slightly increased while it decreased for Romney


Voice of the Voters

Voter's Rank Candidate Trending Causal Tweets
Nov. 6, 2012, 3:30pm EST
1 Barack Obama
Honest Tweet: I voted for Obama because other people's rights are worth more to me than a few tax dollars.
I voted for Obama because I want a President with a moral compass, not one who has been on both sides of nearly every issu ...
After voting for McCain in '08 I voted for Obama this morning and I think he may squeak out a victory this time. This o ...
I voted for Obama because he respects women's rights, gay rights, the middle class, and education! #Foward #VoteObama @ ...
I VOTED for OBAMA because I believe in a forward-thinking country that upholds civil rights FOR ALL. #VoteObama
2 Mitt Romney
Today we vote - either to restore America or to accelerate America's decline. I voted for Romney and restoration.
Welp...I voted for Romney. Solely on the hunch his full name is Mittens. Who wouldn't want a prez named Mittens Romney! ...
The stereotype that all Romney supporters are against gays bothers me. I voted for Romney AND R74. #Equality usElection2012
#ivoted for freedom, for jobs, for growth, and for prosperity. I voted for Romney.
I voted for Romney! I'm British, I didn't mean to, and I prefer Obama, but turns out the e-voting machines really are inse ..


Presidential Debates and Hurricane Effect on Romney and Obama communities

Analysis: Twitter Interactions in topical communities during US Election 2012

Period: Oct 1 to Nov. 3, 2012

Corpus: 4 M tweets and 1.9 M users in interactions


Observations:

Oct. 3, 2012.
The following interaction networks of influential users talking about Romney vs. Obama show the user's sentiment for the target candidate (e.g., Positive attribute in the Obama's network shows positive leaning of the influencer to Obama side).
1.) Before the first presidential debate, the structure of interaction network of top 100 influencers in the two communities looked like following-

Romney's community before 1st debate
     
Obama's community before 1st debate
     


(1.1.) Obama's community appears bit more cohesively connected as compared to Romney's.
(1.2.) There is less population of influential supporters talking about Obama! Is it the effect of aggressive campaigning from Republicans since the week before, as we observed during superior performance of Romney in the first debate? Lets observe the following weeks where social media completely showed a surprising evolution.

Oct. 16, 2012.

2.) After the first presidential debate, the structure of interaction network of top 100 influencers in the two topical communities took completely different shape:

Romney's community after 1st debate
     
Obama's community after 1st debate
     


(2.1.) Obama's community appears to have sparser interaction network as compared to Romney's community after the first debate, it is likely the effect of momentum brought by Romney's impressive performance in the debate.
(2.2.) There is less population of influential supporters talking about Obama among the top 100 influencers and more interestingly, Romney's community is very tightly connected, presenting the coordination potential of very powerful cascades of 'call for action' effect in the network. These networks not only give insights about how the group dynamics evolve, but can be predictor of potential real-life event in the community too. Stay tuned for more analyses after the second presidential debate tonight!

Oct. 18, 2012.

3.) After the second presidential debate, the structure of interaction network of top 100 influencers in the two topical communities again showed surprising shapes even though President Obama performed really well, does it mean that Gallup is getting it right?

Romney's community after 2nd debate
     
Obama's community after 2nd debate
     


(3.1.) Obama's community appears to have sparser interaction network as compared to Romney's community after the second debate, it implies that the Obama campaign still needs to catch up with the momentum brought by Romney's impressive performance in the first debate.
(3.2.) Interestingly, there are more no. of negative influencers in the Romney community, does it mean that the Obama campaign has started coordinated effort to oppose the momentum from Romney's side. Or the Romney's campaign has been keeping up with the coordinated effort as depicted before the first debate? Stay tuned for more analyses before the last presidential debate!

Oct. 21, 2012.

4.) After the second presidential debate, the structure of interaction network of top 100 influencers in the two topical communities showed evolution in the cohesiveness for President Obama, mostly due to his outstanding performance?

Romney's community after 2nd debate, 3 days
     
Obama's community after 2nd debate, 3 days
     


(4.1.) Obama's community appears to have sparser interaction network as compared to Romney's community after the second debate, but it has improved from the state immediately after the second debate. But still, it is observable from these polarized networks that Romney's impressive performance in the first debate still has marks on influencers' minds!
(4.2.) Interestingly, there are increasing no. of negative influencers in the Romney community again as we noted immediately after the second debate, it is suggesting the coordinated efforts from Obama campaign side to oppose the momentum for Romney.

Oct. 23, 2012.

5.) After the final presidential debate, the structure of interaction network of top 100 influencers in the two topical communities provides insights about increased positive cohesiveness for President Obama:

Romney's community during and after 3rd debate, 14 hours
     
Obama's community during and after 3rd debate, 14 hours
     


(5.1.) Obama's community appears to have increased cohesiveness immediately after the final debate performance, now even more strongly cohesive compared to Romney's community. There is substantial gain on the state after the second debate.
(5.2.) Interestingly, there are increasing no. of negative influencers in both the communities as we noted immediately after the second debate also. But the gain in the positive influencers' cohesiveness in the Obama community definitely overwhelms this slight increase, partly due to his superior performance in the last two debates, but also contributed from the potential coordinated efforts from Obama campaign.
We shall update with more analyses on reasoning about such effects shortly, stay tuned!


Nov. 4, 2012.

5.) After the Hurricane Sandy Storm, the structure of interaction network of top 100 influencers in the two topical communities provides insights about increased positive cohesiveness for President Obama:

Romney's community after Hurricane Sandy
     
Obama's community after Hurricane Sandy
     


(6.1.) Obama's community appears to have increased cohesiveness after the final debate performance and the Hurricane Sandy Storm, now even more strongly cohesive compared to Romney's community. There is substantial gain on the state after the final debate. (Please check out 'Insights' tab for the evolution)
(6.2.) Romney's community appears really weak after the storm, can it be due to his negative comments about FEMA's performance and president's role as command-in-chief, though most of the efforts went on to do as best as they could for the emergency response! Interestingly, there are increasing no. of negative influencers in both the communities as well. But the gain in the positive influencers' cohesiveness in the Obama community definitely overwhelms this slight increase, partly due to his superior performance in the last two debates and also acting as the true leader, 'command-in-chief' during national disaster event.


TwiMed (Twitris+Media) Ranking

Rank Candidate @Twitter @Media
May 9, 2012
1 Barack Obama 272118 39973
2 Mitt Romney 90181 19075


Forecasting the Primaries

Twitris uses two indicators extracted from the tweet corpus to forecast the election: (1) popularity of the candidates; (2) positive sentiment of the candidates. Directly using these two indicators have been shown to achieve 80%+ accuracy in the forecast. A more indepth study has been conducted to examine the predictive power of different user groups in predicting the results of Super Tuesday races in 10 states.

The forecast is based on the analysis of tweets in four weeks before the primary day. If the indicators extracted from the four-week data are too close to tell the candidates apart, the indicators extracted from the tweets in the final week before the primary day are employed (e.g, Alabama, Idaho, Ohio).

Bold font highlights the winner. Green background color indicates that the results are consistent with Twitris analysis.

The analysis could not be performed for some candidates in some states (labeled with star*), due to the lack of tweets.

Newt Gingrich Ron Paul Mitt Romney Rick Santorum
popularity positive
sentiment
popularity positive
sentiment
popularity positive
sentiment
popularity positive
sentiment
June 5 California 3.9% 55.2% 9.1% 61.4% 42.6% 47.5% 5.1% 47.9%
Montana*
New Jersey 4.2% 54.4% 7.0% 62.0% 44.9% 48.8% 6.1% 53.0%
New Mexico 7.5% 49.9% 12.7% 50.5% 36.0% 44.7% 7.5% 49.9%
South Dakota*
May 29 Texas 4.8% 54.8% 12.1% 62.7% 39.8% 48.3% 7.5% 60.2%
May 22 Arkansas 6.2% 33.9% 13.3% 54.5% 45.0% 42.1% 28.7% 41.4%
Kentucky 10.3% 59.7% 10.1% 58.6% 44.1% 47.6% 18.1% 40.0%
May 15 Nebraska*
Oregon 8.6% 48.3% 8.5% 47.7% 52.8% 47.4% 25.3% 47.1%
May 8 Indiana*
North Carolina 11.8% 61.5% 11.0% 64.7% 51.9% 52.4% 26.1% 57.6%
West Virginia*
April 24 Connecticut 9.8% 51.3% 14.8% 67.2% 55.3% 55.1% 31.6% 56.1%
Delaware 15.3% 58.8% 14.5% 62.9% 48.5% 50.8% 29.7% 46.0%
New York 10.2% 60.5% 13.6% 58.3% 52.8% 53.9% 31.2% 49.0%
Pennsylvania 11.8% 47.0% * * 46.0% 68.6% 50.5% 57.9%
Rhode Island * * * * 49.2% 57.2% 35.9% 58.3%
April 3 Wisconsin * * * * 77.4% 55.5% * *
Maryland 14.0% 55.7% 10.6% 63.4% 51.6% 61.8% 36.2% 55.7%
District of Columbia 12.0% 55.3% 12.0% 57.9% 39.5%
54.2%
55.1%
52.1%
36.7%
31.3%
51.4%
44.4%
March 24 Louisiana 15.5% 58.0% 12.9% 56.1% 44.2% 54.1% 37.4% 54.9%
March 20 Illinois 11.7% 58.2% 13.1% 54.2% 38.8% 54.5% 36.4% 50.6%
March 18 Puerto Rico*
March 13 Alabama 20.0% 62.5% 12.0% 58.1% 34.8%
41.6%
53.7%
66.5%
34.9%
31.0%
51.8%
65.7%
Hawaii 25.0% 62.1% 19.7% 63.3% 31.2% 61.0% 24.7% 47.7%
Mississippi*
March 10 Kansas 15.8% 61.8% 13.6% 55.7% 38.2% 56.3% 31.9% 46.3%
March 6 Alaska*
Georgia 15.6% 57.5% 13.1% 57.4% 35.3% 57.7% 34.7% 49.1%
Idaho 10.0% 58.2% 15.6% 70.3% 38.6%
49.8%
56.2%
67.3%
36.0%
19.5%
59.4%
65.9%
Massachusetts*
North Dakota*
Ohio 9.4% 55.0% 13.8% 59.7% 35.5%
41.8%
55.6%
62.2%
38.1%
42.4%
51.7%
42.3%
Oklahoma 13.3% 57.8% 15.8% 58.7% 27.6% 52.7% 37.7% 52.6%
Tennessee 17.0% 70.4% 10.4% 61.5% 32.9% 43.6% 36.4% 58.8%
Vermont*
Virginia*
Wyoming*
March 3 Washington 11.6% 57.7% 12.7% 58.3% 29.4% 50.3% 36.4% 48.8%
Feb. 28 Arizona 12.3% 54.9% 17.5% 59.9% 37.6% 52.7% 26.9% 51.4%
Michigan 10.2% 54.5% 14.9% 53.5% 38.3% 50.1% 29.7% 47.0%

People-Content-Network Analysis (PCNA)

Analysis: Twitter Interactions in topical communities during US Election 2012 event

Period: March 1 to March 31, 2012

Corpus: 4.2 M tweets and 1.1 M users in interactions


Observations:

1.) Both Romney and Santorum topical communities showed presence of a core connected network among influential users over the period of Mar 1 to Mar 31, 2012, which in turn depicts the potential to drive the actions of the community users. Interesting to note is that both communities have core set size nearly equal, 40 (non overlapping users) out of top 100 influencers.

2.) Romney community seems to get better organized and connected in small groups over the time as we move towards the end of March, which is likely due to his winning chances after the Super Tuesday and Illinois primary victory. It can be seen from the jump in community density and modularity for the Romney community. Figure 1 shows the changes in the community clusters and table 1 shows various statistics for the People-Content-Network analysis.

3.) Another interesting point to note here is that though both communities showed denser network in the snapshot-3, still the modularity kept decreasing for Santorum community as compared to Romney, which again points to shifting towards better organization in the Romney community influencers.

4.) We analyzed the common set of influential users in the two communities and found only near 10% shift in their political standing, which is likely due to political affinity of users, leading to bias for a particular candidate and party.


We are investigating further role of events occured during the snapshots, which probably caused the Santorum community less organized as compared to Romney community, so please stay tuned for more exciting insights in our next release!


Table 1: Results of PCNA for topical communities surrounding Mitt Romney and Rick Santorum

Group formation in the Communities as function of snapshots:


A.) Romney Influencer Community: shifted towards better modularity and strong connectedness over the time.


.. ..





B.) Santorum Influencer Community: shifted initially for better clustering but ultimately poor modularity and connectedness over the time in comparison to Romney community.


.. ..

What is Twitris+?

A Semantic Social Web application with real-time monitoring and multi-faceted analysis of social signals to provide insights and a framework for situational awareness, in-depth event analysis and coordination, emergency response aid, reputation management etc.

Why Twitris+?

Users are sharing voluminous social data (800M+ active Facebook users, 1B+ tweets/week) through social networking platforms accessible by Web and increasingly via mobile devices. This gives unprecedented opportunity to decision makers-- from corporate analysts to coordinators during emergencies, to answer questions or take actions related to a broad variety of activities and situations: who should they really engage with, how to prioritize posts for actions in the voluminous data stream, what are the needs and who are the resource providers in emergency event, how is corporate brand performing, and does the customer support adequately serve the needs while managing corporate reputation etc. We demonstrate these capabilities using Twitris+.

Key Features

  1. Decision making analytics platform for multi-faceted analyses of social data: spatio-temporal-thematic, people-content-network, sentiment-emotion-subjectivity etc.
  2. Answering questions of interests to corporate analysts and event coordinators
  3. Extraction of insights from social signals: Aggregation and filtering of social data, web resources (news, Wikipedia pages, multimedia), SMS data, followed by applying background knowledge to perform multi-faced analyses.
  4. Applications beyond state-of-the-art research in social-computing, such as in Health 2.0, cyber-physical systems etc.

Research Details

Team:

Alan Smith, Ashutosh Jadhav , Hemant Purohit, Lu Chen, Michael Cooney, Pavan Kapanipathi, Pramod Anatharam, Wenbo Wang (Past Members: Karthik Gomadam, Meena Nagarajan)

Supervision:

Prof. Amit Sheth

Publications & Presentations:

Identifying Seekers and Suppliers in Social Media Communities to Support Crisis Coordination
Hemant Purohit, Carlos Castillo, Fernando Diaz, Amit Sheth, and Patrick MeierHemant Purohit, Andrew Hampton, Shreyansh Bhatt, Valerie L. Shalin, Amit Sheth and John Flach, Journal of CSCW, Springer, 2014 (to appear).

With Whom to Coordinate, Why and How in Ad-hoc Social Media Communities during Crisis Response
Hemant Purohit, Andrew Hampton, Shreyansh Bhatt, Valerie L. Shalin, Amit Sheth and John Flach, ISCRAM, May 2014.

Emergency-Relief Coordination on Social Media: Automatically Matching Resource Requests and Offers.
Hemant Purohit, Carlos Castillo, Fernando Diaz, Amit Sheth, and Patrick Meier, First Monday journal, Vol. 19, Issue 1, Jan 2014.

Twitris v3: From Citizen Sensing to Analysis, Coordination and Action
Hemant Purohit, Amit Sheth, ICWSM-13 Demo track.

What Kind of #Communication is Twitter? Mining #Psycholinguistic Cues for Emergency Coordination
Hemant Purohit, Andrew J. Hampton, Valerie L. Shalin, Amit Sheth, John Flach, Shreyansh Bhatt, Computers in Human Behavior (CHB) journal.

Twitris- a System for Collective Social Intelligence
Amit Sheth, Ashutosh Jadhav, Pavan Kapanipathi, Chen Lu, Hemant Purohit, Gary Alan Smith, Wenbo Wang, Encyclopedia of Social Network Analysis and Mining (ESNAM).

Are Twitter Users Equal in Predicting Elections? A Study of User Groups in Predicting 2012 U.S. Republican Presidential Primaries
Lu Chen, Wenbo Wang and Amit P. Sheth, In Proceedings of the Fourth International Conference on Social Informatics (SocInfo'12), 2012.

Harnessing Twitter 'Big Data' for Automatic Emotion Identification
Wenbo Wang, Lu Chen, Krishnaprasad Thirunarayan and Amit P. Sheth, In Proceedings of International Conference on Social Computing (SocialCom), 2012.

Topical Anomaly Detection from Twitter Stream
Pramod Anantharam, Krishnaprasad Thirunarayan, and Amit Sheth, Research Note: In the Proceedings of ACM Web Science 2012, Evanston, Illinois, June 22-24, 2012.

Extracting Diverse Sentiment Expressions with Target-dependent Polarity from Twitter
Lu Chen, Wenbo Wang, Meenakshi Nagarajan, Shaojun Wang and Amit P. Sheth, In Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM), 2012.

Twitris+: Social Media Analytics Platform for Effective Coordination
A. Smith, A. Sheth, A. Jadhav, H. Purohit, L. Chen, M. Cooney, P. Kapanipathi, P. Anantharam, P. Koneru and W. Wang, NSF SoCS Symposium, 2012.

Discovering Fine-grained Sentiment in Suicide Notes
Wenbo Wang, Lu Chen, Ming Tan, Shaojun Wang, Amit P. Sheth, Biomedical Informatics Insights, vol. 5 (Suppl. 1) pp. 137-145, 2012.

Prediction of Topic Volume on Twitter
Yiye Ruan, Hemant Purohit, Dave Fuhry, Srini Parthasarthy, Amit Sheth, 4th Int'l ACM Conference of Web Science (WebSci), 2012.

Framework for the Analysis of Coordination in Crisis Response
Hemant Purohit, Andrew Hampton, Valerie L. Shalin, Amit Sheth and John Flach, Workshop in conjunction with CSCW-2012.

Personalized Filtering of the Twitter Stream
Pavan Kapanipathi, Fabrizio Orlandi, Amit Sheth, Alexandre Passant, 2nd workshop on Semantic Personalized Information Management at ISWC 2011.

Citizen Sensing - Mining Social Signals & Perceptions: Microsoft Research Faculty Summit
Amit Sheth, Invited Talk at Microsoft Research Faculty Summit 2011, Redmond, WA, July 19, 2011.

Understanding User-Community Engagement by Multi-faceted Features: A Case Study on Twitter
H. Purohit, Y. Ruan, A. Joshi, S. Parthasarathy, A. Sheth, Workshop on Social Media Engagement, in conjunction with WWW 2011.

Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications
Meenakshi Nagarajan,Amit Sheth,Selvam Velmurugan, Proc of the WWW 2011, March 28 - April 1, 2011, Hyderabad, India, ACM.

Twarql: Tapping into the Wisdom of the Crowd
P. Mendes, P. Kapanipathi, and A. Passant, Triplification Challenge 2010 at 6th International Conference on Semantic Systems (I-SEMANTICS), Graz, Austria, 1-3 September 2010. (Winner of Triplification Challenge 2010).

Linked Open Social Signals
Mendes PN, Passant A, Kapanipathi P, Sheth AP, WI2010 IEEE/WIC/ACM International Conference on Web Intelligence (WI-10), Toronto, Canada, Aug. 31 to Sep. 3, 2010.

Understanding User-Generated Content on Social Media
Meenakshi Nagarajan, Understanding User-Generated Content on Social Media, Ph.D. Dissertation, Wright State University, 2010.

Multimodal Social Intelligence in a Real-Time Dashboard System
Daniel Gruhl, Meenakshi Nagarajan, Jan Pieper, Christine Robson, Amit Sheth, VLDB Journal on 'Data Management and Mining for Social Networks and Social Media', 6 (2) 2010.

Twitris 2.0 : Semantically Empowered System for Understanding Perceptions From Social Data
A. Jadhav, H. Purohit, P. Kapanipathi, P. Ananthram, A. Ranabahu, V. Nguyen, P. Mendes, A. G. Smith, M. Cooney, A. Sheth, ISWC 2010 Semantic Web Application Challenge.

A Qualitative Examination of Topical Tweet and Retweet Practices
Meenakshi Nagarajan, Hemant Purohit, Amit Sheth, 4th Int'l AAAI Conference on Weblogs and Social Media, ICWSM 2010, pp. 295-298.

Some Trust Issues in Social Networks and Sensor Networks
Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Henson, Amit Sheth, Proceedings of 2010 International Symposium on Collaborative Technologies and Systems (CTS 2010), Chicago, IL, May 17-21, 2010.

Understanding Events Through Analysis Of Social Media
Amit Sheth, Hemant Purohit, Ashutosh Jadhav, Pavan Kapanipathi and Lu Chen, Technical Report, Kno.e.sis Center, 2010.

Spatio-Temporal-Thematic Analysis of Citizen-Sensor Data - Challenges and Experiences
Meenakshi Nagarajan, Karthik Gomadam, Amit Sheth, Ajith Ranabahu, Raghava Mutharaju and Ashutosh Jadhav, Tenth International Conference on Web Information Systems Engineering, October 5-7, 2009, 539 - 553.

Citizen Sensing, Social Signals, and Enriching Human Experience
A. Sheth, IEEE Internet Computing, July/August 2009, pp. 80-85.

Analysis and Monetization of Social Data
Amit Sheth, Panel on 'Semantifying Social Networks,' Semantic Technology Conference, June 16, 2009, San Jose, CA.

Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A Comprehensive Path Towards Event Monitoring and Situational Awareness
Amit Sheth, From E-Gov to Connected Governance: the Role of Cloud Computing, Web 2.0 and Web 3.0 Semantic Technologies, Fall Church, VA, February 17, 2009.

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Articles and Resources

OneIndia News (Sept 20, 2014)

Twitris supported highly impactful #JKFloodRelief initiative by VOICE team at @InCrisisRelief, via Need-to-Rescue and Influencer Analysis tools. Digital volunteers use social media, come together during Jammu floods

Hindustan Times (Sept 9, 2014)

Twitris supported highly impactful #JKFloodRelief initiative by VOICE team at InCrisisRelief.org via Social Media monitoring and Influencer Analyses. Digital soldiers emerge heroes in Kashmir flood rescue

CrisisNET blog by Ushahidi (Jun 18, 2014)

Integration of our crisis response research into CrisisNET project for leveraging social media- Who Helps When Crisis Hits?

Tutorial at the SIAM conference SDM-14 (Apr 24, 2014)

Leveraging Social Media and Web of Data for Crisis Response Coordination

iRevolution (Nov 11, 2013)

Initiative of Twitris team to support MicroMappers app, for helping UNOCHA- Digital Humanitarians: From Haiti Earthquake to Typhoon Yolanda

DNA (Oct 12, 2013)

Initiative of Twitris team for digital volunteer-driven Crisis Map- Google's Person Finder and Google Crisis Response Map for Phailin to help with crisis information

The Hindu news media (Jun 27, 2013)

Initiative of Twitris team for digital volunteer-driven Crisis Map- Using crisis mapping to aid Uttarakhand , Are we missing out on tech-aided disaster management in Uttarakhand?

SemanticWeb.com (November 8, 2012)

Election 2012: The Semantic Recap

New Tech Post and Technology Voice (April 9, 2012)

twitris: Social Media Analysis with Semantic Web Technology

Dayton Business Journal (Mar 7, 2012)

Wright State wins patent for analyzing text messages

Wright State University News Room (November 14, 2011)

Wright State research seeks sense from social media to aid in emergencies

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