Karma Flows Version: 0.9 Beta
Country: United States
Date From: 2023-06-01
Date To: 2023-06-30
News Sentiment Bias: -5.636853
Macro Economic Karma Index: ___
Government Karma Index: ___
GraphSAGE Karma Prediction: ___Karma Flows - United States
Karma Flows
Interactive Ensemble Clustering for Graphs
Node colours & clusters indicate latent community membership. Blue edges denote positive Karma. Red edges denote negative Karma. Click on nodes to reveal ranking order within latent communities. Click on edges to reveal associated news artifacts and Goldstein ratings. Wider edges denote stronger connections. Nodes can represent People, Places, Organizations as well as themes and taxonomy concepts. Themes and concepts are shown in brackets.
Karma Flows - If It Shines It Leads
Positive Sentiment Priority View
Positive Karma edges are rendered on top.
Karma Geolocation
Top 12 Influential Persons
| person | degree | lookup |
|---|---|---|
| Joe Biden | 917 | Wolfram Alpha Google Wikipedia |
| Donald Trump | 754 | Wolfram Alpha Google Wikipedia |
| Vladimir Putin | 544 | Wolfram Alpha Google Wikipedia |
| Ron Desantis | 458 | Wolfram Alpha Google Wikipedia |
| Antony Blinken | 394 | Wolfram Alpha Google Wikipedia |
| Mike Pence | 254 | Wolfram Alpha Google Wikipedia |
| Barack Obama | 245 | Wolfram Alpha Google Wikipedia |
| Andy Gregory | 233 | Wolfram Alpha Google Wikipedia |
| Chris Christie | 231 | Wolfram Alpha Google Wikipedia |
| Kevin Mccarthy | 229 | Wolfram Alpha Google Wikipedia |
| Elon Musk | 226 | Wolfram Alpha Google Wikipedia |
| Nikki Haley | 224 | Wolfram Alpha Google Wikipedia |
Top 12 Influential Locations
| location | degree | lookup |
|---|---|---|
| United States | 2047 | Wolfram Alpha Google Wikipedia |
| Washington, Washington, United States | 1537 | Wolfram Alpha Google Wikipedia |
| New York, United States | 1421 | Wolfram Alpha Google Wikipedia |
| Russia | 1312 | Wolfram Alpha Google Wikipedia |
| Ukraine | 1303 | Wolfram Alpha Google Wikipedia |
| America | 1231 | Wolfram Alpha Google Wikipedia |
| China | 1154 | Wolfram Alpha Google Wikipedia |
| California, United States | 1087 | Wolfram Alpha Google Wikipedia |
| Florida, United States | 932 | Wolfram Alpha Google Wikipedia |
| Texas, United States | 828 | Wolfram Alpha Google Wikipedia |
| United Kingdom | 762 | Wolfram Alpha Google Wikipedia |
| Moscow, Moskva, Russia | 710 | Wolfram Alpha Google Wikipedia |
Top 12 Influential Organizations
| organization | degree | lookup |
|---|---|---|
| Reuters | 1158 | Wolfram Alpha Google Wikipedia |
| White House | 911 | Wolfram Alpha Google Wikipedia |
| Associated Press | 729 | Wolfram Alpha Google Wikipedia |
| 634 | Wolfram Alpha Google Wikipedia | |
| Cnn | 605 | Wolfram Alpha Google Wikipedia |
| New York Times | 531 | Wolfram Alpha Google Wikipedia |
| Supreme Court | 528 | Wolfram Alpha Google Wikipedia |
| European Union | 484 | Wolfram Alpha Google Wikipedia |
| United Nations | 473 | Wolfram Alpha Google Wikipedia |
| 422 | Wolfram Alpha Google Wikipedia | |
| Justice Department | 392 | Wolfram Alpha Google Wikipedia |
| Department Of Justice | 274 | Wolfram Alpha Google Wikipedia |
Top 30 Themes & Aura
Reference Aura Bias -3.109993| Theme | Degree | Aura | Compensated Aura |
|---|---|---|---|
| Leader | 1362 | -2.4517412 | 0.65825176 |
| General Politics | 1358 | -2.4828758 | 0.62711716 |
| President | 1125 | -2.6254895 | 0.4845035 |
| Crisis | 1024 | -4.573564 | -1.4635711 |
| Crisis And Safety | 1021 | -4.370207 | -1.2602139 |
| Manmade Disaster Implied | 970 | -3.7521536 | -0.64216065 |
| General Government | 956 | -2.7092292 | 0.40076375 |
| Economic Policy Uncertainty: Policy Government | 831 | -3.1174986 | -0.0075056553 |
| Forests Rivers Oceans | 755 | -2.092091 | 1.0179019 |
| Fragility Conflict And Violence | 715 | -4.0714517 | -0.9614587 |
| Public Sector Management | 664 | -3.7952297 | -0.6852367 |
| Officials | 660 | -3.514823 | -0.40482998 |
| Economic Policy Uncertainty: Economy Historic | 618 | -1.3568966 | 1.7530963 |
| Education | 610 | -1.986441 | 1.123552 |
| World Languages Russia | 593 | -4.4751453 | -1.3651524 |
| Legislation | 590 | -3.8157036 | -0.70571065 |
| Security Services | 578 | -5.425131 | -2.3151379 |
| Trial | 578 | -4.79636 | -1.686367 |
| Armed Conflict | 578 | -3.870466 | -0.760473 |
| Minister | 568 | -2.0183945 | 1.0915985 |
| Economic Policy Categorical: National Security | 559 | -3.4695911 | -0.35959816 |
| Economic Policy Uncertainty: Policy Law | 548 | -4.0209327 | -0.9109397 |
| Justice | 537 | -4.1094823 | -0.9994893 |
| Political Policy | 519 | -2.2845054 | 0.8254876 |
| General Health | 518 | -3.090099 | 0.019893885 |
| Economic Policy Uncertainty: Policy Political | 500 | -2.9420655 | 0.1679275 |
| Police | 488 | -5.5978374 | -2.4878445 |
| Information And Communication Technologies | 479 | -3.3064947 | -0.19650173 |
| Ethnicity American | 472 | -2.3409643 | 0.76902866 |
| Medical | 468 | -3.183469 | -0.073476076 |
Legend
\[\begin{aligned} \mathrm Karma_{flow} = \frac{\mathrm{Sentiment}}{\mid \mathrm{Sentiment} \mid} \cdot \sqrt{ Goldstein^2 + \mathrm{Sentiment}^2 + \mathrm{Strength}^2} \\ \text{where} \\ \mathrm{Sentiment} \ \text{is the average tone between two entities on the graph across all events connecting them} \ and \\ \mathrm{Goldstein} \ \text{is the average severity of events between two entities on the graph across all events connecting them} \ and \\ \mathrm{Strength} \ \text{is aggregate connection strength between two entities on the graph across all events connecting them.} \\ \\ \\ \\ \mathrm Karma_{cameo} = \frac{\mathrm{Sentiment}}{\mid \mathrm{Sentiment} \mid} \cdot \sqrt{ Goldstein^2 + \mathrm{Sentiment}^2 + \mathrm{Citations}^2} \\ \text{where} \\ \mathrm{Sentiment} \ \text{is the average tone across all mentions of the Event(s) in all citations} \ and \\ \mathrm{Goldstein} \ \text{is the severity of the Event(s)} \ and \\ \mathrm{Citations} \ \text{is the volume of mentions the Event(s) has received} and \\ \mathrm{Event(s)} \ \text{are the event or events at a specific longitude and latitude.} \\ \\ \\ \\ \\ \mathrm Sentiment Bias = \frac{\sum_{i=1}^{n} \frac{\mathrm{Sentiment_{i}}}{\mid \mathrm{Sentiment_{i}} \mid} \cdot \mathrm{\sqrt{ \mathrm{Sentiment_{i}}^2 + \mathrm{Strength_{i}}^2}}}{n} \\ \text{where} \\ \mathrm{Sentiment_{i}} \ \text{is the average tone between two entities on the graph for an edge i} \ and \\ \mathrm{Strength_{i}} \ \text{is the aggregate connection strength between two entities on the graph for an edge i.} \ and \\ \mathrm{n} \ \text{is the number of egdes on the graph.} \\ \\ \\ \\ \end{aligned}\]