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Wikipedia:WikiProject Conservatism

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    Welcome to WikiProject Conservatism! Whether you're a newcomer or regular, you'll receive encouragement and recognition for your achievements with conservatism-related articles. This project does not extol any point of view, political or otherwise, other than that of a neutral documentarian. Partly due to this, the project's scope has long become that of conservatism broadly construed, taking in a healthy periphery of (e.g., more academic) articles for contextualization.

    Major alerts

    A broad collection of discussions that could lead to significant changes of related articles

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    Articles for deletion

    • 19 May 2025 – Dummycrats (talk · edit · hist) was AfDed by मल्ल (t · c); see discussion (5 participants)
    • 16 May 2025 – Joseph K. Wood (talk · edit · hist) was AfDed by Vanderwaalforces (t · c); see discussion (8 participants)
    • 10 May 2025 – Ed Lopez (talk · edit · hist) was AfDed by Leonstojka (t · c); see discussion (8 participants; relisted)

    Redirects for discussion

    Good article nominees

    Requests for comments

    Requested moves

    Articles to be merged

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    Watchlists

    WatchAll (Excerpt)
    Excerpt from watchlist concerning all the articles in the project's scope
    Note that your own edits, minor edits, and bot edits are hidden in this tab

    List of abbreviations (help):
    D
    Edit made at Wikidata
    r
    Edit flagged by ORES
    N
    New page
    m
    Minor edit
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    23 May 2025

    22 May 2025

    21 May 2025

    20 May 2025

    For this watchlist but about 3X in length, visit: Wikipedia:WikiProject Conservatism/All recent changes
    WatchHot (Excerpt)
    A list of 10 related articles with the most (recent) edits total
    142 edits Mahathir Mohamad
    98 edits Coalition (Australia)
    93 edits Department of Government Efficiency
    89 edits Heil Hitler (song)
    84 edits People's Party (Bulgaria)
    75 edits Imran Khan
    71 edits Traditionalism (perennialism)
    67 edits Donald Trump
    52 edits Glenn Diesen
    49 edits US federal agencies targeted by DOGE

    These are the articles that have been edited the most within the last seven days. Last updated 23 May 2025 by HotArticlesBot.



    List of abbreviations (help):
    D
    Edit made at Wikidata
    r
    Edit flagged by ORES
    N
    New page
    m
    Minor edit
    b
    Bot edit
    (±123)
    Page byte size change

    23 May 2025

    22 May 2025

    21 May 2025

    For this watchlist but about 5X in length, visit: Wikipedia:WikiProject Conservatism/Hot articles recent changes
    WatchPop (Excerpt)
    A list of 500 related articles with the most (recent) views total

    This is a list of pages in the scope of Wikipedia:WikiProject Conservatism along with pageviews.

    To report bugs, please write on the Community tech bot talk page on Meta.

    List

    Period: 2025-04-01 to 2025-04-30

    Total views: 72,197,281

    Updated: 13:03, 6 May 2025 (UTC)

    Rank Page title Views Daily average Assessment Importance
    1 Donald Trump 2,049,096 68,303 B High
    2 Karoline Leavitt 1,391,792 46,393 B Low
    3 Elon Musk 1,302,026 43,400 GA Low
    4 JD Vance 1,069,291 35,643 B Mid
    5 Nayib Bukele 1,035,986 34,532 GA Low
    6 Laura Loomer 796,919 26,563 C Low
    7 Pete Hegseth 747,077 24,902 B Low
    8 Kristi Noem 638,473 21,282 B Low
    9 George Santos 518,446 17,281 B Low
    10 Pam Bondi 477,221 15,907 C Low
    11 Strom Thurmond 439,190 14,639 B Mid
    12 Douglas Murray (author) 427,803 14,260 C Low
    13 Vladimir Putin 406,828 13,560 B High
    14 Ronald Reagan 385,167 12,838 FA Top
    15 Charlie Kirk 362,443 12,081 C Low
    16 George W. Bush 361,108 12,036 B High
    17 Narendra Modi 331,778 11,059 GA Top
    18 Project 2025 312,262 10,408 B Mid
    19 Stephen Miller (political advisor) 311,341 10,378 B Low
    20 Winston Churchill 310,507 10,350 GA Top
    21 Mel Gibson 305,642 10,188 B Mid
    22 Richard Nixon 301,687 10,056 FA High
    23 History of tariffs in the United States 295,150 9,838 B Mid
    24 Marco Rubio 286,805 9,560 B Mid
    25 Manosphere 282,668 9,422 B Low
    26 Republican Party (United States) 282,432 9,414 B Top
    27 George H. W. Bush 282,211 9,407 B High
    28 Dwight D. Eisenhower 277,940 9,264 B High
    29 Theodore Roosevelt 276,997 9,233 B High
    30 Marjorie Taylor Greene 276,874 9,229 GA Low
    31 Benjamin Netanyahu 274,067 9,135 B Mid
    32 Department of Government Efficiency 262,126 8,737 B High
    33 Conservative Party of Canada 246,409 8,213 B High
    34 Barron Trump 240,472 8,015 B Low
    35 Linda McMahon 238,535 7,951 B Low
    36 Nancy Mace 237,645 7,921 B Low
    37 Family of Donald Trump 214,645 7,154 B Low
    38 Margaret Thatcher 206,076 6,869 A Top
    39 Charlton Heston 205,232 6,841 B Low
    40 Jordan Peterson 204,321 6,810 B Low
    41 William McKinley 204,032 6,801 FA Low
    42 French Revolution 199,162 6,638 B Top
    43 Gerald Ford 199,091 6,636 C High
    44 Herbert Hoover 198,552 6,618 B Mid
    45 Cold War 198,265 6,608 C Top
    46 Candace Owens 192,865 6,428 B Low
    47 Bharatiya Janata Party 191,768 6,392 GA Top
    48 Marine Le Pen 186,547 6,218 B Low
    49 Stephen Harper 184,631 6,154 GA High
    50 Woke 181,297 6,043 B Top
    51 Dana Perino 178,902 5,963 C Low
    52 Curtis Yarvin 177,311 5,910 C High
    53 2025 German federal election 176,075 5,869 B High
    54 Zionism 175,594 5,853 B Low
    55 Jon Voight 175,202 5,840 C Low
    56 People's Party of Canada 173,304 5,776 C Low
    57 Kelsey Grammer 165,710 5,523 B Low
    58 Liz Truss 161,537 5,384 FA Mid
    59 John Wayne 158,561 5,285 B Low
    60 Second presidency of Donald Trump 154,696 5,156 C Low
    61 Rishi Sunak 153,584 5,119 B High
    62 Chuck Norris 152,262 5,075 B Low
    63 Robert Duvall 150,386 5,012 B Low
    64 Tony Hinchcliffe 148,357 4,945 B Low
    65 Fyodor Dostoevsky 148,292 4,943 B Low
    66 Javier Milei 140,609 4,686 B Mid
    67 John Kennedy (Louisiana politician) 140,015 4,667 C Low
    68 Brooke Rollins 138,246 4,608 Start Low
    69 Chiang Kai-shek 136,487 4,549 C Low
    70 Megyn Kelly 135,145 4,504 B Low
    71 Chuck Grassley 134,247 4,474 C Mid
    72 Rupert Murdoch 132,543 4,418 B Low
    73 Shinzo Abe 132,501 4,416 B Mid
    74 Nick Fuentes 132,400 4,413 B Low
    75 Muhammad Ali Jinnah 130,997 4,366 FA High
    76 Stephen Baldwin 130,687 4,356 B Low
    77 Francisco Franco 130,601 4,353 C Mid
    78 Kemi Badenoch 130,006 4,333 B Low
    79 Kayleigh McEnany 129,320 4,310 C Low
    80 Imran Khan 127,754 4,258 C Low
    81 Oren Cass 127,040 4,234 C Low
    82 Dick Cheney 125,185 4,172 C Mid
    83 James Caan 124,910 4,163 C Low
    84 Ben Shapiro 124,007 4,133 C Mid
    85 QAnon 123,647 4,121 GA Mid
    86 Matt Gaetz 123,282 4,109 B Low
    87 Shirley Temple 123,038 4,101 B Low
    88 Mike Johnson 122,530 4,084 C Mid
    89 Constitution of the United States 122,247 4,074 B High
    90 Tom Homan 122,025 4,067 C Low
    91 Mike Waltz 121,558 4,051 C Low
    92 Grover Cleveland 120,444 4,014 FA Mid
    93 Trump derangement syndrome 119,857 3,995 C Mid
    94 Steve Bannon 118,110 3,937 B Mid
    95 Red states and blue states 117,487 3,916 C Mid
    96 Rashtriya Swayamsevak Sangh 117,295 3,909 B Top
    97 Trumpism 116,631 3,887 B Mid
    98 Anna Paulina Luna 116,126 3,870 B Low
    99 Reform UK 115,787 3,859 C High
    100 Deng Xiaoping 115,707 3,856 B Low
    101 John Roberts 114,801 3,826 B High
    102 Boris Johnson 114,238 3,807 B High
    103 Brett Cooper (commentator) 113,268 3,775 Start Low
    104 John Malkovich 112,210 3,740 C Low
    105 New World Order conspiracy theory 112,202 3,740 GA High
    106 Sarah Palin 110,405 3,680 C Mid
    107 Warren G. Harding 109,923 3,664 FA Low
    108 Calvin Coolidge 109,771 3,659 FA High
    109 James Stewart 109,673 3,655 GA Low
    110 Ted Cruz 109,229 3,640 B Mid
    111 Maxime Bernier 108,778 3,625 C Low
    112 John McCain 106,993 3,566 FA Mid
    113 Recep Tayyip Erdoğan 106,842 3,561 B High
    114 Charles de Gaulle 106,522 3,550 B Mid
    115 Friedrich Merz 104,929 3,497 C Mid
    116 Amy Coney Barrett 103,516 3,450 C Low
    117 Clark Gable 103,510 3,450 B Low
    118 James Woods 102,541 3,418 Start Low
    119 Lara Trump 102,071 3,402 C Low
    120 Hillbilly Elegy 101,410 3,380 B Low
    121 William Howard Taft 101,312 3,377 FA Mid
    122 Thomas Sowell 100,958 3,365 C Mid
    123 Viktor Orbán 100,557 3,351 C Mid
    124 Fox News 99,529 3,317 C Mid
    125 Tucker Carlson 99,189 3,306 B High
    126 Ayn Rand 98,901 3,296 GA Mid
    127 Ron DeSantis 98,635 3,287 B Mid
    128 Condoleezza Rice 98,474 3,282 B Mid
    129 Nigel Farage 98,072 3,269 B Mid
    130 Alternative for Germany 98,021 3,267 C Low
    131 Taliban 97,707 3,256 B High
    132 Falun Gong 97,399 3,246 B Mid
    133 Dark Enlightenment 97,090 3,236 Start Mid
    134 James A. Garfield 96,990 3,233 FA Low
    135 Mitt Romney 96,340 3,211 FA High
    136 McCarthyism 95,460 3,182 C High
    137 Jesse Watters 95,387 3,179 Start Low
    138 Neoliberalism 94,026 3,134 B Top
    139 Patricia Heaton 93,958 3,131 C Low
    140 Elise Stefanik 93,268 3,108 B Low
    141 Conservative Party (UK) 92,933 3,097 B High
    142 Clarence Thomas 92,481 3,082 B Mid
    143 Mike Pence 91,647 3,054 B Mid
    144 Otto von Bismarck 91,133 3,037 B High
    145 Russell Vought 91,101 3,036 Start Mid
    146 1964 United States presidential election 90,940 3,031 C Mid
    147 Susie Wiles 88,306 2,943 C Low
    148 Riley Gaines 87,582 2,919 B Mid
    149 Atal Bihari Vajpayee 86,792 2,893 GA High
    150 Lauren Boebert 85,905 2,863 B Low
    151 Sean Hannity 85,746 2,858 B Mid
    152 Rand Paul 85,409 2,846 GA Mid
    153 Samuel Alito 84,649 2,821 C Mid
    154 Jeanine Pirro 82,985 2,766 B Low
    155 Bo Derek 82,762 2,758 Start Low
    156 Far-right politics 82,693 2,756 B Low
    157 Angela Merkel 81,661 2,722 GA High
    158 Mitch McConnell 81,114 2,703 B Mid
    159 Kevin Hassett 81,003 2,700 Start Mid
    160 Milton Friedman 80,743 2,691 GA High
    161 George Wallace 79,957 2,665 B Mid
    162 Paul von Hindenburg 79,429 2,647 C Mid
    163 Great Replacement conspiracy theory 79,327 2,644 C Top
    164 Iran–Contra affair 78,843 2,628 GA Low
    165 Rachel Campos-Duffy 78,537 2,617 Start Low
    166 Dave Mustaine 78,182 2,606 C Low
    167 Charles Lindbergh 78,080 2,602 B Low
    168 Anthony Scaramucci 77,626 2,587 C Low
    169 Daily Mail 77,617 2,587 B Mid
    170 Lee Hsien Loong 76,410 2,547 C Mid
    171 Gadsden flag 75,855 2,528 B Low
    172 Sebastian Gorka 75,776 2,525 C Unknown
    173 Libertarianism 75,592 2,519 B High
    174 Itamar Ben-Gvir 75,520 2,517 C Mid
    175 Fourteen Words 75,332 2,511 Start Low
    176 Cicero 74,438 2,481 B Mid
    177 Donald Rumsfeld 74,206 2,473 B Mid
    178 Truth Social 73,472 2,449 B Low
    179 Éamon de Valera 73,436 2,447 B High
    180 People's Action Party 73,386 2,446 C Mid
    181 Andy Street 73,101 2,436 B Mid
    182 Arthur Wellesley, 1st Duke of Wellington 73,048 2,434 B Low
    183 Make America Great Again 72,763 2,425 B High
    184 David Cameron 72,713 2,423 B Top
    185 Laura Ingraham 72,051 2,401 C Mid
    186 The Heritage Foundation 71,830 2,394 B High
    187 Generation 71,798 2,393 B Mid
    188 Bing Crosby 71,413 2,380 B Low
    189 Ron Paul 71,236 2,374 C Mid
    190 Gary Sinise 71,215 2,373 C Low
    191 Brian Mulroney 70,879 2,362 B High
    192 T. S. Eliot 70,804 2,360 B Low
    193 Anders Behring Breivik 70,757 2,358 C Low
    194 False or misleading statements by Donald Trump 70,706 2,356 B Low
    195 Whig Party (United States) 70,699 2,356 C Low
    196 Karl Malone 70,605 2,353 Start Low
    197 Sarah Huckabee Sanders 70,392 2,346 C Low
    198 Dmitry Medvedev 70,338 2,344 C High
    199 David Horowitz 70,154 2,338 B Mid
    200 Ben Carson 70,059 2,335 C Low
    201 Craig T. Nelson 69,811 2,327 Start Unknown
    202 Edward Teller 68,943 2,298 FA Low
    203 House of Bourbon 68,719 2,290 B High
    204 Doug Ford 68,684 2,289 B Low
    205 Right-wing politics 67,847 2,261 C Top
    206 Greg Gutfeld 67,780 2,259 C Low
    207 John Locke 67,705 2,256 B Top
    208 Brett Kavanaugh 67,558 2,251 B High
    209 Rudy Giuliani 66,816 2,227 B Mid
    210 Dan Bongino 66,359 2,211 C Mid
    211 Neville Chamberlain 65,832 2,194 FA Mid
    212 Shigeru Ishiba 65,662 2,188 B Low
    213 Mahathir Mohamad 65,578 2,185 GA High
    214 Benjamin Harrison 65,306 2,176 FA Low
    215 Conservatism 65,301 2,176 B Top
    216 Gamergate (harassment campaign) 64,968 2,165 C Mid
    217 David Duke 64,829 2,160 B Mid
    218 Steve Hilton 64,762 2,158 C Mid
    219 Kellyanne Conway 64,560 2,152 B Low
    220 Jordan Bardella 64,289 2,142 C High
    221 Victor Davis Hanson 64,064 2,135 B Mid
    222 Jair Bolsonaro 63,992 2,133 B Mid
    223 Chester A. Arthur 63,728 2,124 FA Low
    224 Barbara Stanwyck 63,607 2,120 B Low
    225 Lindsey Graham 63,560 2,118 C Low
    226 National Rally 63,424 2,114 GA High
    227 Greg Abbott 63,191 2,106 B Mid
    228 Mike Huckabee 63,100 2,103 B Mid
    229 Neoconservatism 62,988 2,099 C Top
    230 Left–right political spectrum 62,977 2,099 C Top
    231 Lisa Murkowski 62,820 2,094 C High
    232 Brothers of Italy 62,799 2,093 B Mid
    233 Mary Matalin 62,770 2,092 C Low
    234 Nawaz Sharif 62,597 2,086 B Unknown
    235 Gary Cooper 62,209 2,073 FA Mid
    236 Progressive Conservative Party of Canada 62,130 2,071 Start High
    237 Anthony Eden 61,759 2,058 B Mid
    238 Christopher Rufo 61,732 2,057 C Low
    239 Right-wing populism 61,532 2,051 B High
    240 Nancy Reagan 60,981 2,032 B Mid
    241 Terri Schiavo case 60,569 2,018 GA Low
    242 Newt Gingrich 60,074 2,002 B High
    243 Danielle Smith 60,058 2,001 B Unknown
    244 Liberal Party of Australia 59,944 1,998 C High
    245 Theresa May 59,881 1,996 B Mid
    246 Spiro Agnew 59,031 1,967 FA Mid
    247 Tammy Bruce 58,426 1,947 Start Low
    248 Angie Harmon 58,374 1,945 C Low
    249 Barry Goldwater 58,156 1,938 B High
    250 Last Man Standing (American TV series) 57,787 1,926 B Low
    251 Patrick Bet-David 57,683 1,922 C Low
    252 Deus vult 57,666 1,922 Start Low
    253 John Major 57,619 1,920 B High
    254 Vinayak Damodar Savarkar 57,559 1,918 B High
    255 L. K. Advani 57,528 1,917 B High
    256 Byron Donalds 57,449 1,914 C Low
    257 Mike Lindell 57,373 1,912 C Low
    258 Dave Ramsey 56,716 1,890 C Unknown
    259 Dilip Ghosh (politician) 56,655 1,888 B Low
    260 Proud Boys 56,601 1,886 C Low
    261 Bob Hope 56,358 1,878 B Low
    262 Rutherford B. Hayes 56,251 1,875 FA Low
    263 Elon Musk salute controversy 55,943 1,864 B Low
    264 Bob Katter 55,718 1,857 C Low
    265 Capitalism 54,886 1,829 C Top
    266 Andrew Scheer 54,598 1,819 C Unknown
    267 Bill O'Reilly (political commentator) 54,563 1,818 B Mid
    268 Ben Stein 54,241 1,808 C Low
    269 Melissa Joan Hart 54,157 1,805 B Low
    270 Rebel News 53,814 1,793 C Low
    271 Tom Clancy 53,459 1,781 C Low
    272 Coalition (Australia) 53,427 1,780 C High
    273 Denis Leary 53,285 1,776 C NA
    274 Nikki Haley 53,158 1,771 B Low
    275 Nicolas Sarkozy 52,835 1,761 B High
    276 First presidency of Donald Trump 52,759 1,758 B Low
    277 Oliver North 52,189 1,739 C Mid
    278 Corey Lewandowski 52,068 1,735 C Low
    279 Newsmax 52,036 1,734 B Low
    280 White supremacy 51,927 1,730 B Low
    281 Pat Buchanan 51,893 1,729 B Mid
    282 Lee Zeldin 51,716 1,723 B Low
    283 The Times of India 51,642 1,721 C Mid
    284 Park Chung Hee 51,610 1,720 C Low
    285 John Thune 51,496 1,716 C Low
    286 John Ratcliffe 51,430 1,714 C Low
    287 Deportation in the second presidency of Donald Trump 51,147 1,704 B Low
    288 Ashley Moody 50,929 1,697 C Unknown
    289 The Wall Street Journal 50,883 1,696 B Mid
    290 David Mamet 50,805 1,693 C Low
    291 List of Canadian conservative leaders 50,752 1,691 List Mid
    292 Ann Coulter 50,525 1,684 B Mid
    293 Harold Macmillan 50,451 1,681 B High
    294 Pat Sajak 49,840 1,661 C Low
    295 Marc Andreessen 49,724 1,657 C Mid
    296 Pauline Hanson's One Nation 49,664 1,655 C Mid
    297 Scott Baio 49,370 1,645 Start Low
    298 Bob Dole 49,331 1,644 B Low
    299 Tea Party movement 49,286 1,642 C Mid
    300 Dan Quayle 49,231 1,641 B Mid
    301 Pat Boone 49,008 1,633 C Low
    302 Kelly Loeffler 48,763 1,625 B Low
    303 Neil Gorsuch 48,576 1,619 B Mid
    304 Richard Grenell 48,324 1,610 C Low
    305 Ray Bradbury 47,964 1,598 B Low
    306 W. B. Yeats 47,551 1,585 FA Low
    307 Roger Stone 47,283 1,576 C Low
    308 People Power Party (South Korea) 47,200 1,573 C High
    309 International Democracy Union 47,048 1,568 Start Top
    310 Tomi Lahren 47,021 1,567 Start Low
    311 John Howard 46,871 1,562 B Mid
    312 Aleksandr Solzhenitsyn 46,764 1,558 B Mid
    313 Mark Rutte 46,586 1,552 C High
    314 Political appointments of the second Trump administration 46,431 1,547 List Low
    315 Paul Ryan 46,369 1,545 C Mid
    316 Benjamin Disraeli 46,219 1,540 FA Top
    317 Matt Walsh (political commentator) 46,165 1,538 C Low
    318 AI slop 46,081 1,536 C Low
    319 Rush Limbaugh 45,717 1,523 B High
    320 Zia-ul-Haq 45,585 1,519 B High
    321 Donald Trump and fascism 45,554 1,518 B Mid
    322 Reform Party of Canada 45,466 1,515 B High
    323 Ted Nugent 45,455 1,515 C Low
    324 Antonin Scalia 45,440 1,514 FA High
    325 Critical race theory 45,388 1,512 C Low
    326 Harmeet Dhillon 45,280 1,509 Start Low
    327 Jean-Marie Le Pen 45,242 1,508 B Mid
    328 Alpha and beta male 45,235 1,507 C Low
    329 Turning Point USA 44,901 1,496 C Low
    330 Dinesh D'Souza 44,619 1,487 B Mid
    331 Adam Kinzinger 44,126 1,470 C Low
    332 Breitbart News 43,939 1,464 C Mid
    333 GypsyCrusader 43,746 1,458 C High
    334 António de Oliveira Salazar 43,460 1,448 B Mid
    335 Rick Scott 43,030 1,434 C Low
    336 Federalist Party 42,977 1,432 C Low
    337 Jackson Hinkle 42,940 1,431 B Low
    338 Kalergi Plan 42,881 1,429 Start Mid
    339 The Epoch Times 42,877 1,429 B Low
    340 Liz Cheney 42,800 1,426 B High
    341 Groypers 42,755 1,425 B Low
    342 Kevin McCarthy 42,624 1,420 C Low
    343 The Daily Wire 42,556 1,418 C Low
    344 John Cornyn 42,431 1,414 B Low
    345 Martin Heidegger 42,215 1,407 C Low
    346 Booker T. Washington 42,203 1,406 B Low
    347 Winsome Earle-Sears 42,164 1,405 C Low
    348 Alice Weidel 41,800 1,393 C Low
    349 Laura Bush 41,795 1,393 GA Low
    350 United Russia 41,667 1,388 B High
    351 Morgan Ortagus 41,432 1,381 C Unknown
    352 Jacob Rees-Mogg 41,216 1,373 C Low
    353 Jeb Bush 41,196 1,373 B Low
    354 Victoria Spartz 41,170 1,372 C Low
    355 Grey Wolves (organization) 41,157 1,371 B Mid
    356 Phil Robertson 41,071 1,369 C Low
    357 Dixiecrat 41,048 1,368 Start Mid
    358 Elisabeth Hasselbeck 40,884 1,362 C Low
    359 Liberal Democratic Party (Japan) 40,848 1,361 C High
    360 Sheldon Adelson 40,833 1,361 C Low
    361 New York Post 40,794 1,359 C Low
    362 Christian nationalism 40,280 1,342 Start High
    363 Jacobitism 40,183 1,339 B High
    364 Ezra Levant 39,880 1,329 Start Unknown
    365 William F. Buckley Jr. 39,830 1,327 B Top
    366 First impeachment of Donald Trump 39,797 1,326 B High
    367 Renaud Camus 39,763 1,325 Start Unknown
    368 1924 United States presidential election 39,747 1,324 C Low
    369 Alt-right 39,460 1,315 C Mid
    370 John C. Calhoun 39,451 1,315 FA Top
    371 Ross Douthat 39,441 1,314 Start Low
    372 James Cagney 39,415 1,313 B Low
    373 Likud 39,264 1,308 C Low
    374 Milo Yiannopoulos 38,941 1,298 C Low
    375 Katter's Australian Party 38,823 1,294 C Low
    376 Chris Christie 38,783 1,292 C Low
    377 Mullah Omar 38,726 1,290 B High
    378 Thom Tillis 38,590 1,286 B Low
    379 Ginger Rogers 38,510 1,283 C Unknown
    380 Canadian Alliance 38,425 1,280 B Mid
    381 Aleksandr Dugin 38,424 1,280 C Mid
    382 Abdul Qadeer Khan 38,418 1,280 C Low
    383 John A. Macdonald 38,408 1,280 FA High
    384 Mark Levin 38,304 1,276 B High
    385 Tim Scott 38,222 1,274 C Low
    386 Christian Democratic Union of Germany 38,165 1,272 C High
    387 The Daily Telegraph 38,165 1,272 C Low
    388 Christopher Luxon 38,119 1,270 B Unknown
    389 D. H. Lawrence 38,006 1,266 B Unknown
    390 Protectionism 37,953 1,265 B Mid
    391 John Layfield 37,865 1,262 B Low
    392 Jemima Goldsmith 37,768 1,258 C Unknown
    393 Conservatism in the United States 37,687 1,256 B Top
    394 Matthew Whitaker 36,970 1,232 C Low
    395 Fianna Fáil 36,475 1,215 B Low
    396 Edward Heath 36,406 1,213 B High
    397 Mia Love 36,347 1,211 C Low
    398 Ustaše 36,200 1,206 C High
    399 Laissez-faire 36,131 1,204 C Top
    400 Reaganomics 35,957 1,198 B Mid
    401 Chris Williamson (TV personality) 35,788 1,192 Stub Low
    402 Stacey Dash 35,730 1,191 C Low
    403 Law and Justice 35,293 1,176 C High
    404 Menachem Begin 35,200 1,173 B Mid
    405 John Birch Society 35,129 1,170 C Low
    406 Benny Johnson (columnist) 35,069 1,168 Start Low
    407 Flannery O'Connor 35,029 1,167 A Low
    408 Piggate 34,963 1,165 C Low
    409 Franz von Papen 34,855 1,161 B Low
    410 Classical liberalism 34,814 1,160 B Top
    411 National Party of Australia 34,814 1,160 C High
    412 Progressivism 34,792 1,159 C Mid
    413 Enoch Powell 34,782 1,159 C High
    414 Guy Benson 34,754 1,158 C Low
    415 Facebook–Cambridge Analytica data scandal 34,736 1,157 C Unknown
    416 Joe Scarborough 34,723 1,157 B Low
    417 Fred MacMurray 34,581 1,152 C Low
    418 Edmund Burke 34,494 1,149 B Top
    419 Frank Bruno 34,470 1,149 Start Unknown
    420 Jane Russell 34,463 1,148 B Low
    421 Michael Steele 34,173 1,139 B Low
    422 Tommy Tuberville 34,158 1,138 B Low
    423 Reform Party of the United States of America 34,090 1,136 C Low
    424 Friedrich Hayek 33,997 1,133 B Top
    425 Bourbon Restoration in France 33,974 1,132 C High
    426 David Frum 33,883 1,129 C Low
    427 Nick Land 33,660 1,122 C Low
    428 Thomas Mann 33,646 1,121 C Mid
    429 Hillsdale College 33,623 1,120 C Low
    430 Tony Abbott 33,620 1,120 C Mid
    431 Tom Cotton 33,566 1,118 C Low
    432 Traditionalist Catholicism 33,450 1,115 C Top
    433 Deep state conspiracy theory in the United States 33,395 1,113 Start Low
    434 Islamophobia 33,296 1,109 C Mid
    435 Betsy DeVos 33,072 1,102 C Mid
    436 Philip Anschutz 33,063 1,102 C Low
    437 Hindutva 33,060 1,102 B Top
    438 Blue Dog Coalition 33,048 1,101 C Low
    439 2022 Conservative Party of Canada leadership election 33,001 1,100 C Low
    440 Ayaan Hirsi Ali 32,984 1,099 B Low
    441 Don King 32,977 1,099 B Low
    442 Dana Loesch 32,949 1,098 B Low
    443 Shiv Sena 32,884 1,096 C Unknown
    444 Alessandra Mussolini 32,878 1,095 B Low
    445 Johnny Ramone 32,370 1,079 C Low
    446 UK Independence Party 32,345 1,078 B Low
    447 Thomas Massie 32,205 1,073 B Low
    448 Justice and Development Party (Turkey) 32,077 1,069 B Low
    449 Liberty University 32,007 1,066 B Mid
    450 Deportation and removal from the United States 31,809 1,060 C Unknown
    451 Roger Ailes 31,722 1,057 C Mid
    452 White genocide conspiracy theory 31,642 1,054 B Low
    453 Will Cain 31,631 1,054 Start Mid
    454 T. D. Jakes 31,601 1,053 C Unknown
    455 Original sin 31,470 1,049 C Low
    456 Primogeniture 31,467 1,048 Start Low
    457 Preston Manning 31,400 1,046 B Mid
    458 Doug Collins (politician) 31,396 1,046 Start Low
    459 March 2025 American deportations of Venezuelans 31,338 1,044 C Low
    460 William Rehnquist 31,289 1,042 B High
    461 Public Square (company) 31,266 1,042 Start Low
    462 Loretta Young 31,209 1,040 C Low
    463 Profumo affair 31,153 1,038 FA Mid
    464 Fred Thompson 31,058 1,035 B Low
    465 John Bolton 31,054 1,035 C Mid
    466 Reagan (2024 film) 31,005 1,033 C Low
    467 Allan Shivers 30,989 1,032 Start Low
    468 John Boehner 30,962 1,032 Start High
    469 Walter Brennan 30,728 1,024 C Low
    470 Bharat Rashtra Samithi 30,686 1,022 C Low
    471 Richard Hanania 30,474 1,015 C Low
    472 Koch family 30,295 1,009 Start High
    473 Glenn Beck 30,272 1,009 B Mid
    474 Islam in the United Kingdom 30,210 1,007 B Low
    475 Charles Koch 30,191 1,006 B Low
    476 Redneck 30,097 1,003 C Low
    477 Jeff Sessions 30,040 1,001 Start Unknown
    478 Alec Douglas-Home 29,983 999 FA Low
    479 Political spectrum 29,938 997 C Top
    480 The Fountainhead 29,842 994 FA Low
    481 Marie Windsor 29,821 994 Start Unknown
    482 Geert Wilders 29,777 992 B Low
    483 Gretchen Whitmer kidnapping plot 29,747 991 C Low
    484 Dennis Miller 29,694 989 Start Low
    485 Race and crime in the United States 29,628 987 C Mid
    486 Islamism 29,614 987 B High
    487 Infowars 29,587 986 C Low
    488 12 Rules for Life 29,507 983 B Mid
    489 Kim Reynolds 29,506 983 C Low
    490 Meghan McCain 29,437 981 C Low
    491 Cultural Marxism conspiracy theory 29,323 977 B Low
    492 Jason Kenney 29,313 977 C Unknown
    493 Freedom Caucus 29,259 975 C Low
    494 Blaire White 29,122 970 Start Low
    495 The Gateway Pundit 29,092 969 C Unknown
    496 Progressive Conservative Party of Ontario 28,960 965 B Mid
    497 Blue Tory 28,927 964 Stub Low
    498 Andrew Sullivan 28,915 963 B Low
    499 Richard B. Spencer 28,887 962 C Low
    500 Anti-communism 28,848 961 B Mid


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    In The Signpost

    One of various articles to this effect
    The Right Stuff
    July 2018
    DISCUSSION REPORT
    WikiProject Conservatism Comes Under Fire

    By Lionelt

    WikiProject Conservatism was a topic of discussion at the Administrators' Noticeboard/Incident (AN/I). Objective3000 started a thread where he expressed concern regarding the number of RFC notices posted on the Discussion page suggesting that such notices "could result in swaying consensus by selective notification." Several editors participated in the relatively abbreviated six hour discussion. The assertion that the project is a "club for conservatives" was countered by editors listing examples of users who "profess no political persuasion." It was also noted that notification of WikiProjects regarding ongoing discussions is explicitly permitted by the WP:Canvassing guideline.

    At one point the discussion segued to feedback about The Right Stuff. Member SPECIFICO wrote: "One thing I enjoy about the Conservatism Project is the handy newsletter that members receive on our talk pages." Atsme praised the newsletter as "first-class entertainment...BIGLY...first-class...nothing even comes close...it's amazing." Some good-natured sarcasm was offered with Objective3000 observing, "Well, they got the color right" and MrX's followup, "Wow. Yellow is the new red."

    Admin Oshwah closed the thread with the result "definitely not an issue for ANI" and directing editors to the project Discussion page for any further discussion. Editor's note: originally the design and color of The Right Stuff was chosen to mimic an old, paper newspaper.

    Add the Project Discussion page to your watchlist for the "latest RFCs" at WikiProject Conservatism Watch (Discuss this story)

    ARTICLES REPORT
    Margaret Thatcher Makes History Again

    By Lionelt

    Margaret Thatcher is the first article promoted at the new WikiProject Conservatism A-Class review. Congratulations to Neveselbert. A-Class is a quality rating which is ranked higher than GA (Good article) but the criteria are not as rigorous as FA (Featued article). WikiProject Conservatism is one of only two WikiProjects offering A-Class review, the other being WikiProject Military History. Nominate your article here. (Discuss this story)
    RECENT RESEARCH
    Research About AN/I

    By Lionelt

    Reprinted in part from the April 26, 2018 issue of The Signpost; written by Zarasophos

    Out of over one hundred questioned editors, only twenty-seven (27%) are happy with the way reports of conflicts between editors are handled on the Administrators' Incident Noticeboard (AN/I), according to a recent survey . The survey also found that dissatisfaction has varied reasons including "defensive cliques" and biased administrators as well as fear of a "boomerang effect" due to a lacking rule for scope on AN/I reports. The survey also included an analysis of available quantitative data about AN/I. Some notable takeaways:

    • 53% avoided making a report due to fearing it would not be handled appropriately
    • "Otherwise 'popular' users often avoid heavy sanctions for issues that would get new editors banned."
    • "Discussions need to be clerked to keep them from raising more problems than they solve."

    In the wake of Zarasophos' article editors discussed the AN/I survey at The Signpost and also at AN/I. Ironically a portion of the AN/I thread was hatted due to "off-topic sniping." To follow-up the problems identified by the research project the Wikimedia Foundation Anti-Harassment Tools team and Support and Safety team initiated a discussion. You can express your thoughts and ideas here.

    (Discuss this story)

    Delivered: ~~~~~


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    WikiProject Conservatism

    Is Wikipedia Politically Biased? Perhaps


    A monthly overview of recent academic research about Wikipedia and other Wikimedia projects, also published as the Wikimedia Research Newsletter.


    Report by conservative think-tank presents ample quantitative evidence for "mild to moderate" "left-leaning bias" on Wikipedia

    A paper titled "Is Wikipedia Politically Biased?"[1] answers that question with a qualified yes:

    [...] this report measures the sentiment and emotion with which political terms are used in [English] Wikipedia articles, finding that Wikipedia entries are more likely to attach negative sentiment to terms associated with a right-leaning political orientation than to left-leaning terms. Moreover, terms that suggest a right-wing political stance are more frequently connected with emotions of anger and disgust than those that suggest a left-wing stance. Conversely, terms associated with left-leaning ideology are more frequently linked with the emotion of joy than are right-leaning terms.
    Our findings suggest that Wikipedia is not entirely living up to its neutral point of view policy, which aims to ensure that content is presented in an unbiased and balanced manner.

    The author (David Rozado, an associate professor at Otago Polytechnic) has published ample peer-reviewed research on related matters before, some of which was featured e.g. in The Guardian and The New York Times. In contrast, the present report is not peer-reviewed and was not posted in an academic venue, unlike most research we cover here usually. Rather, it was published (and possibly commissioned) by the Manhattan Institute, a conservative US think tank, which presumably found its results not too objectionable. (Also, some – broken – URLs in the PDF suggest that Manhattan Institute staff members were involved in the writing of the paper.) Still, the report indicates an effort to adhere to various standards of academic research publications, including some fairly detailed descriptions of the methods and data used. It is worth taking it more seriously than, for example, another recent report that alleged a different form of political bias on Wikipedia, which had likewise been commissioned by an advocacy organization and authored by an academic researcher, but was met with severe criticism by the Wikimedia Foundation (who called it out for "unsubstantiated claims of bias") and volunteer editors (see prior Signpost coverage).

    That isn't to say that there can't be some questions about the validity of Rozado's results, and in particular about how to interpret them. But let's first go through the paper's methods and data sources in more detail.

    Determining the sentiment and emotion in Wikipedia's coverage

    The report's main results regarding Wikipedia are obtained as follows:

    "We first gather a set of target terms (N=1,628) with political connotations (e.g., names of recent U.S. presidents, U.S. congressmembers, U.S. Supreme Court justices, or prime ministers of Western countries) from external sources. We then identify all mentions in English-language Wikipedia articles of those terms.

    We then extract the paragraphs in which those terms occur to provide the context in which the target terms are used and feed a random sample of those text snippets to an LLM (OpenAI’s gpt-3.5-turbo), which annotates the sentiment/emotion with which the target term is used in the snippet. To our knowledge, this is the first analysis of political bias in Wikipedia content using modern LLMs for annotation of sentiment/emotion."

    The sentiment classification rates the mention of a terms as negative, neutral or positive. (For the purpose of forming averages this is converted into a quantitative scale from -1 to +1.) See the end of this review for some concrete examples from the paper's published dataset.

    The emotion classification uses "Ekman’s six basic emotions (anger, disgust, fear, joy, sadness, and surprise) plus neutral."

    The annotation method used appears to be an effort to avoid the shortcomings of popular existing sentiment analysis techniques, which often only rate the overall emotional stance of a given text overall without determining whether it actually applies to a specific entity mentioned in it (or in some cases even fail to handle negations, e.g. by classifying "I am not happy" as a positive emotion). Rozado justifies the "decision to use automated annotation" (which presumably rendered considerable cost savings, also by resorting to OpenAI's older GPT 3.5 model rather than the more powerful but more expensive GPT-4 API released in March 2023) citing "recent evidence showing how top-of-the-rank LLMs outperform crowd workers for text-annotation tasks such as stance detection." This is indeed becoming a more widely used choice for text classification. But Rozado appears to have skipped the usual step of evaluating the accuracy of this automated method (and possibly improving the prompts it used) against a gold standard sample from (human) expert raters.

    Selecting topics to examine for bias

    As for the selection of terms whose Wikipedia coverage to annotate with this classifier, Rozado does a lot of due diligence to avoid cherry-picking: "To reduce the degrees of freedom of our analysis, we mostly use external sources of terms [including Wikipedia itself, e.g. its list of members of the 11th US Congress] to conceptualize a political category into left- and right-leaning terms, as well as to choose the set of terms to include in each category." This addresses an important source of researcher bias.

    Overall, the study arrives at 12 different groups of such terms:

    • 8 of these refer to people (e.g. US presidents, US senators, UK members of parliament, US journalists).
    • Two are about organizations (US think tanks and media organizations).
    • The other two groups contain "Terms that describe political orientation", i.e. expressions that carry a left-leaning or right-leaning meaning themselves:
      • 18 "political leanings" (where "Rightists" receives the lowest average sentiment and "Left winger" the highest), and
      • 21 "extreme political ideologies" (where "Ultraconservative" scores lowest and "radical-left" has the highest – but still slightly negative – average sentiment)

    What is "left-leaning" and "right-leaning"?

    As discussed, Rozado's methods for generating these lists of people and organizations seem reasonably transparent and objective. It gets a bit murkier when it comes to splitting them into "left-leaning" and "right-leaning", where the chosen methods remain unclear and/or questionable in some cases. Of course there is a natural choice available for US Congress members, where the confines of the US two-party system mean that the left-right spectrum can be easily mapped easily to Democrats vs. Republicans (disregarding a small number of independents or libertarians).

    In other cases, Rozado was able to use external data about political leanings, e.g. "a list of politically aligned U.S.-based journalists" from Politico. There may be questions about construct validity here (e.g. it classifies Glenn Greenwald or Andrew Sullivan as "journalists with the left"), but at least this data is transparent and determined by a source not invested in the present paper's findings.

    But for example the list of UK MPs used contains politicians from 14 different parties (plus independents). Even if one were to confine the left vs. right labels to the two largest groups in the UK House of Commons (Tories vs. Labour and Co-operative Party, which appears to have been the author's choice judging from Figure 5), the presence of a substantial number of parliamentarians from other parties to the left or right of those would make the validity of this binary score more questionable than in the US case. Rozado appears to acknowledge a related potential issue in a side remark when trying to offer an explanation for one of the paper's negative results (no bias) in this case: "The disparity of sentiment associations in Wikipedia articles between U.S. Congressmembers and U.K. MPs based on their political affiliation may be due in part to the higher level of polarization in the U.S. compared to the U.K."

    Tony Abbott.
    Most negative sentiment among Western leaders: Former Australian PM Tony Abbott
    Scott Morrison.
    Most positive sentiment among Western leaders: Former Australian PM Scott Morrison

    This kind of question become even more complicated for the "Leaders of Western Countries" list (where Tony Abbott scored the most negative average sentiment, and José Luis Rodríguez Zapatero and Scott Morrison appear to be in a tie for the most positive average sentiment). Most of these countries do not have a two-party system either. Sure, their leaders usually (like in the UK case) hail from one of the two largest parties, one of which is more to the left and the another more to the right. But it certainly seems to matter for the purpose of Rozado's research question whether that major party is more moderate (center-left or center-right, with other parties between it and the far left or far right) or more radical (i.e. extending all the way to the far-left or far-right spectrum of elected politicians).

    What's more, the analysis for this last group compares political orientations across multiple countries. Which brings us to a problem that Wikipedia's Jimmy Wales had already pointed to back in 2006 in response a conservative US blogger who had argued that there was "a liberal bias in many hot-button topic entries" on English Wikipedia:

    "The Wikipedia community is very diverse, from liberal to conservative to libertarian and beyond. If averages mattered, and due to the nature of the wiki software (no voting) they almost certainly don't, I would say that the Wikipedia community is slightly more liberal than the U.S. population on average, because we are global and the international community of English speakers is slightly more liberal than the U.S. population. ... The idea that neutrality can only be achieved if we have some exact demographic matchup to [the] United States of America is preposterous."

    We already discussed this issue in our earlier reviews of a notable series of papers by Greenstein and Zhu (see e.g.: "Language analysis finds Wikipedia's political bias moving from left to right", 2012), which had relied on a US-centric method of defining left-leaning and right-leaning (namely, a corpus derived from the US Congressional Record). Those studies form a large part of what Rozado cites as "[a] substantial body of literature [that]—albeit with some exceptions—has highlighted a perceived bias in Wikipedia content in favor of left-leaning perspectives." (The cited exception is a paper[2] that had found "a small to medium size coverage bias against [members of parliament] from the center-left parties in Germany and in France", and identified patterns of "partisan contributions" as a plausible cause.)

    Similarly, 8 out of the 10 groups of people and organizations analyzed in Rozado's study are from the US (the two exceptions being the aforementioned lists of UK MPs and leaders of Western countries).

    In other words, one potential reason for the disparities found by Rozado might simply be that he is measuring an international encyclopedia with a (largely) national yardstick of fairness. This shouldn't let us dismiss his findings too easily. But it is a bit disappointing that this possibility is nowhere addressed in the paper, even though Rozado diligently discusses some other potential limitations of the results. E.g. he notes that "some research has suggested that conservatives themselves are more prone to negative emotions and more sensitive to threats than liberals", but points out that the general validity of those research results remains doubtful.

    Another limitation is that a simple binary left vs. right classification might be hiding factors that can shed further light on bias findings. Even in the US with its two-party system, political scientists and analysts have long moved to less simplistic measures of political orientations. A widely used one is the NOMINATE method which assigns members of the US Congress continuous scores based on their detailed voting record, one of which corresponds to the left-right spectrum as traditionally understood. One finding based on that measure that seems relevant in context of the present study is the (widely discussed but itself controversial) asymmetric polarization thesis, which argues that "Polarization among U.S. legislators is asymmetric, as it has primarily been driven by a substantial rightward shift among congressional Republicans since the 1970s, alongside a much smaller leftward shift among congressional Democrats" (as summarized in the linked Wikipedia article). If, for example, higher polarization was associated with negative sentiments, this could be a potential explanation for Rozado's results. Again, this has to remain speculative, but it seems another notable omission in the paper's discussion of limitations.

    What does "bias" mean here?

    A fundamental problem of this study, which, to be fair, it shares with much fairness and bias research (in particular on Wikipedia's gender gap, where many studies similarly focus on binary comparisons that are likely to successfully appeal to an intuitive sense of fairness) consists of justifying its answers to the following two basic questions:

    1. What would be a perfectly fair baseline, a result that makes us confident to call Wikipedia unbiased?
    2. If there are deviations from that baseline (often labeled disparities, gaps or biases), what are the reasons for that – can we confidently assume they were caused by Wikipedia itself (e.g. demographic imbalances in Wikipedia's editorship), or are they more plausibly attributed to external factors?

    Regarding 1 (defining a baseline of unbiasedness), Rozado simply assumes that this should imply statistically indistinguishable levels of average sentiment between left and right-leaning terms. However, as cautioned by one leading scholar on quantitative measures of bias, "the 'one true fairness definition' is a wild goose chase" – there are often multiple different definitions available that can all be justified on ethical grounds, and are often contradictory. Above, we already alluded to two potentially diverging notions of political unbiasedness for Wikipedia (using an international instead of US metric for left vs right leaning, and taking into account polarization levels for politicians).

    But yet another question, highly relevant for Wikipedians interested in addressing the potential problems reported in this paper, is how much its definition lines up with Wikipedia's own definition of neutrality. Rozado clearly thinks that it does:

    Wikipedia’s neutral point of view (NPOV) policy aims for articles in Wikipedia to be written in an impartial and unbiased tone. Our results suggest that Wikipedia’s NPOV policy is not achieving its stated goal of political-viewpoint neutrality in Wikipedia articles.

    WP:NPOV indeed calls for avoiding subjective language and expressing judgments and opinions in Wikipedia's own voice, and Rozado's findings about the presence of non-neutral sentiments and emotions in Wikipedia articles are of some concern in that regard. However, that is not the core definition of NPOV. Rather, it refers to "representing fairly, proportionately, and, as far as possible, without editorial bias, all the significant views that have been published by reliable sources on a topic." What if the coverage of the terms examined by Rozado (politicians, etc.) in those reliable sources, in their aggregate, were also biased in the sense of Rozado's definition? US progressives might be inclined to invoke the snarky dictum "reality has a liberal bias" by comedian Stephen Colbert. Of course, conservatives might object that Wikipedia's definition of reliable sources (having "a reputation for fact-checking and accuracy") is itself biased, or applied in a biased way by Wikipedians. For some of these conservatives (at least those that are not also conservative feminists) it may be instructive to compare examinations of Wikipedia's gender gaps, which frequently focus on specific groups of notable people like in Rozado's study. And like him, they often implicitly assume a baseline of unbiasedness that implies perfect symmetry in Wikipedia's coverage – i.e. the absence of gaps or disparities. Wikipedians often object that this is in tension with the aforementioned requirement to reflect coverage in reliable sources. For example, Wikipedia's list of Fields medalists (the "Nobel prize of Mathematics") is 97% male – not because of Wikipedia editors' biases against women, but because of a severe gender imbalance in the field of mathematics that is only changing slowly, i.e. factors outside Wikipedia's influence.

    All this brings us to question 2. above (causality). While Rozado uses carefully couched language in this regard ("suggests" etc, e.g. "These trends constitute suggestive evidence of political bias embedded in Wikipedia articles"), such qualifications are unsurprisingly absent in much of the media coverage of this study (see also this issue's In the media). For example, the conservative magazine The American Spectator titled its article about the paper "Now We've Got Proof that Wikipedia is Biased."

    Commendably, the paper is accompanied by a published dataset, consisting of the analyzed Wikipedia text snippets together with the mentioned term and the sentiment or emotion identified by the automated annotation. For illustration, below are the sentiment ratings for mentions of the Yankee Institute for Public Policy (the last term in the dataset, as a non-cherry-picked example), with the term bolded:

    Dataset excerpt: Wikipedia paragraphs with sentiment for "Yankee Institute for Public Policy"
    positive "Carol Platt Liebau is president of the Yankee Institute for Public Policy.Liebau named new president of Yankee Institute She is also an attorney, political analyst, and conservative commentator. Her book Prude: How the Sex-Obsessed Culture Damages Girls (and America, Too!) was published in 2007."
    neutral "Affiliates

    Regular members are described as ""full-service think tanks"" operating independently within their respective states.

    Alabama: Alabama Policy Institute
    Alaska: Alaska Policy Forum
    [...]
    Connecticut: Yankee Institute for Public Policy
    [...]
    Wisconsin: MacIver Institute for Public Policy, Badger Institute, Wisconsin Institute for Law and Liberty, Institute for Reforming Government
    Wyoming: Wyoming Liberty Group"
    positive "The Yankee Institute for Public Policy is a free market, limited government American think tank based in Hartford, Connecticut, that researches Connecticut public policy questions. Organized as a 501(c)(3), the group's stated mission is to ""develop and advocate for free market, limited government public policy solutions in Connecticut."" Yankee was founded in 1984 by Bernard Zimmern, a French entrepreneur who was living in Norwalk, Connecticut, and Professor Gerald Gunderson of Trinity College. The organization is a member of the State Policy Network."
    neutral "He is formerly Chairman of the Yankee Institute for Public Policy. On November 3, 2015, he was elected First Selectman in his hometown of Stonington, Connecticut, which he once represented in Congress. He defeated the incumbent, George Crouse. Simmons did not seek reelection in 2019."
    negative "In Connecticut the union is closely identified with liberal Democratic politicians such as Governor Dannel Malloy and has clashed frequently with fiscally conservative Republicans such as former Governor John G. Rowland as well as the Yankee Institute for Public Policy, a free-market think tank."
    positive "In 2021, after leaving elective office, she was named a Board Director of several organizations. One is the Center for Workforce Inclusion, a national nonprofit in Washington, DC, that works to provide meaningful employment opportunities for older individuals. Another is the William F. Buckley Program at Yale, which aims to promote intellectual diversity, expand political discourse on campus, and expose students to often-unvoiced views at Yale University. She also serves on the Board of the Helicon Foundation, which explores chamber music in its historical context by presenting and producing period performances, including an annual subscription series of four Symposiums in New York featuring both performance and discussion of chamber music. She is also a Board Director of the American Hospital of Paris Foundation, which provides funding support for the operations of the American Hospital of Paris and functions as the link between the Hospital and the United States, funding many collaborative and exchange programs with New York-Presbyterian Hospital. She is also a Fellow of the Yankee Institute for Public Policy, a research and citizen education organization that focuses on free markets and limited government, as well as issues of transparency and good governance."
    positive "He was later elected chairman of the New Hampshire Republican State Committee, a position he held from 2007 to 2008. When he was elected he was 34 years old, making him the youngest state party chairman in the history of the United States at the time. His term as chairman included the 2008 New Hampshire primary, the first primary in the 2008 United States presidential election. He later served as the executive director of the Yankee Institute for Public Policy for five years, beginning in 2009. He is the author of a book about the New Hampshire primary, entitled Granite Steps, and the founder of the immigration reform advocacy group Americans By Choice."

    Briefly


    Other recent publications

    Other recent publications that could not be covered in time for this issue include the items listed below. Contributions, whether reviewing or summarizing newly published research, are always welcome.

    How English Wikipedia mediates East Asian historical disputes with Habermasian communicative rationality

    From the abstract: [3]

    "We compare the portrayals of Balhae, an ancient kingdom with contested contexts between [South Korea and China]. By comparing Chinese, Korean, and English Wikipedia entries on Balhae, we identify differences in narrative construction and framing. Employing Habermas’s typology of human action, we scrutinize related talk pages on English Wikipedia to examine the strategic actions multinational contributors employ to shape historical representation. This exploration reveals the dual role of online platforms in both amplifying and mediating historical disputes. While Wikipedia’s policies promote rational discourse, our findings indicate that contributors often vacillate between strategic and communicative actions. Nonetheless, the resulting article approximates Habermasian ideals of communicative rationality."

    From the paper:

    "The English Wikipedia presents Balhae as a multi-ethnic kingdom, refraining from emphasizing the dominance of a single tribe. In comparison to the two aforementioned excerpts [from Chinese and Korean Wikipedia], the lead section of the English Wikipedia concentrates more on factual aspects of history, thus excluding descriptions that might entail divergent interpretations. In other words, this account of Balhae has thus far proven acceptable to a majority of Wikipedians from diverse backgrounds. [...] Compared to other language versions, the English Wikipedia forthrightly acknowledges the potential disputes regarding Balhae's origin, ethnic makeup, and territorial boundaries, paving the way for an open and transparent exploration of these contested historical subjects. The separate 'Balhae controversies' entry is dedicated to unpacking the contentious issues. In essence, the English article adopts a more encyclopedic tone, aligning closely with Wikipedia's mission of providing information without imposing a certain perspective."

    (See also excerpts)

    Facebook/Meta's "No Language Left Behind" translation model used on Wikipedia

    From the abstract of this publication by a large group of researchers (most of them affiliated with Meta AI):[4]

    "Focusing on improving the translation qualities of a relatively small group of high-resource languages comes at the expense of directing research attention to low-resource languages, exacerbating digital inequities in the long run. To break this pattern, here we introduce No Language Left Behind—a single massively multilingual model that leverages transfer learning across languages. [...] Compared with the previous state-of-the-art models, our model achieves an average of 44% improvement in translation quality as measured by BLEU. By demonstrating how to scale NMT [neural machine translation] to 200 languages and making all contributions in this effort freely available for non-commercial use, our work lays important groundwork for the development of a universal translation system."

    "Four months after the launch of NLLB-200 [in 2022], Wikimedia reported that our model was the third most used machine translation engine used by Wikipedia editors (accounting for 3.8% of all published translations) (https://web.archive.org/web/20221107181300/https://nbviewer.org/github/wikimedia-research/machine-translation-service-analysis-2022/blob/main/mt_service_comparison_Sept2022_update.ipynb). Compared with other machine translation services and across all languages, articles translated with NLLB-200 has the lowest percentage of deletion (0.13%) and highest percentage of translation modification kept under 10%."

    "Which Nigerian-Pidgin does Generative AI speak?" – only the BBC's, not Wikipedia's

    From the abstract:[5]

    "Naija is the Nigerian-Pidgin spoken by approx. 120M speakers in Nigeria [...]. Although it has mainly been a spoken language until recently, there are currently two written genres (BBC and Wikipedia) in Naija. Through statistical analyses and Machine Translation experiments, we prove that these two genres do not represent each other (i.e., there are linguistic differences in word order and vocabulary) and Generative AI operates only based on Naija written in the BBC genre. In other words, Naija written in Wikipedia genre is not represented in Generative AI."

    The paper's findings are consistent with an analysis by the Wikimedia Foundation's research department that compared the number of Wikipedia articles to the number of speakers for the top 20 most-spoken languages, where Naija stood out as one of the most underrepresented.

    "[A] surprising tension between Wikipedia's principle of safeguarding against self-promotion and the scholarly norm of 'due credit'"

    From the abstract:[6]

    Although Wikipedia offers guidelines for determining when a scientist qualifies for their own article, it currently lacks guidance regarding whether a scientist should be acknowledged in articles related to the innovation processes to which they have contributed. To explore how Wikipedia addresses this issue of scientific "micro-notability", we introduce a digital method called Name Edit Analysis, enabling us to quantitatively and qualitatively trace mentions of scientists within Wikipedia's articles. We study two CRISPR-related Wikipedia articles and find dynamic negotiations of micro-notability as well as a surprising tension between Wikipedia’s principle of safeguarding against self-promotion and the scholarly norm of “due credit.” To reconcile this tension, we propose that Wikipedians and scientists collaborate to establish specific micro-notability guidelines that acknowledge scientific contributions while preventing excessive self-promotion.

    See also coverage of a different paper that likewise analyzed Wikipedia's coverage of CRISPR: "Wikipedia as a tool for contemporary history of science: A case study on CRISPR"

    "How article category in Wikipedia determines the heterogeneity of its editors"

    From the abstract:[7]

    " [...] the quality of Wikipedia articles rises with the number of editors per article as well as a greater diversity among them. Here, we address a not yet documented potential threat to those preconditions: self-selection of Wikipedia editors to articles. Specifically, we expected articles with a clear-cut link to a specific country (e.g., about its highest mountain, "national" article category) to attract a larger proportion of editors of that nationality when compared to articles without any specific link to that country (e.g., "gravity", "universal" article category), whereas articles with a link to several countries (e.g., "United Nations", "international" article category) should fall in between. Across several language versions, hundreds of different articles, and hundreds of thousands of editors, we find the expected effect [...]"

    "What do they make us see:" The "cultural bias" of GLAMs is worse on Wikidata

    From the abstract:[8]

    "Large cultural heritage datasets from museum collections tend to be biased and demonstrate omissions that result from a series of decisions at various stages of the collection construction. The purpose of this study is to apply a set of ethical criteria to compare the level of bias of six online databases produced by two major art museums, identifying the most biased and the least biased databases. [...] For most variables the online system database is more balanced and ethical than the API dataset and Wikidata item collection of the two museums."

    References

    1. ^ Rozado, David (June 2024). "Is Wikipedia Politically Biased?". Manhattan Institute. Dataset: https://doi.org/10.5281/zenodo.10775984
    2. ^ Kerkhof, Anna; Münster, Johannes (2019-10-02). "Detecting coverage bias in user-generated content". Journal of Media Economics. 32 (3–4): 99–130. doi:10.1080/08997764.2021.1903168. ISSN 0899-7764.
    3. ^ Jee, Jonghyun; Kim, Byungjun; Jun, Bong Gwan (2024). "The role of English Wikipedia in mediating East Asian historical disputes: the case of Balhae". Asian Journal of Communication: 1–20. doi:10.1080/01292986.2024.2342822. ISSN 0129-2986. Closed access icon (access for Wikipedia Library users)
    4. ^ Costa-jussà, Marta R.; Cross, James; Çelebi, Onur; Elbayad, Maha; Heafield, Kenneth; Heffernan, Kevin; Kalbassi, Elahe; Lam, Janice; Licht, Daniel; Maillard, Jean; Sun, Anna; Wang, Skyler; Wenzek, Guillaume; Youngblood, Al; Akula, Bapi; Barrault, Loic; Gonzalez, Gabriel Mejia; Hansanti, Prangthip; Hoffman, John; Jarrett, Semarley; Sadagopan, Kaushik Ram; Rowe, Dirk; Spruit, Shannon; Tran, Chau; Andrews, Pierre; Ayan, Necip Fazil; Bhosale, Shruti; Edunov, Sergey; Fan, Angela; Gao, Cynthia; Goswami, Vedanuj; Guzmán, Francisco; Koehn, Philipp; Mourachko, Alexandre; Ropers, Christophe; Saleem, Safiyyah; Schwenk, Holger; Wang, Jeff; NLLB Team (June 2024). "Scaling neural machine translation to 200 languages". Nature. 630 (8018): 841–846. Bibcode:2024Natur.630..841N. doi:10.1038/s41586-024-07335-x. ISSN 1476-4687. PMC 11208141. PMID 38839963.
    5. ^ Adelani, David Ifeoluwa; Doğruöz, A. Seza; Shode, Iyanuoluwa; Aremu, Anuoluwapo (2024-04-30). "Which Nigerian-Pidgin does Generative AI speak?: Issues about Representativeness and Bias for Multilingual and Low Resource Languages". arXiv:2404.19442 [cs.CL].
    6. ^ Simons, Arno; Kircheis, Wolfgang; Schmidt, Marion; Potthast, Martin; Stein, Benno (2024-02-28). "Who are the "Heroes of CRISPR"? Public science communication on Wikipedia and the challenge of micro-notability". Public Understanding of Science. doi:10.1177/09636625241229923. ISSN 0963-6625. PMID 38419208. blog post
    7. ^ Oeberst, Aileen; Ridderbecks, Till (2024-01-07). "How article category in Wikipedia determines the heterogeneity of its editors". Scientific Reports. 14 (1): 740. Bibcode:2024NatSR..14..740O. doi:10.1038/s41598-023-50448-y. ISSN 2045-2322. PMC 10772120. PMID 38185716.
    8. ^ Zhitomirsky-Geffet, Maayan; Kizhner, Inna; Minster, Sara (2022-01-01). "What do they make us see: a comparative study of cultural bias in online databases of two large museums". Journal of Documentation. 79 (2): 320–340. doi:10.1108/JD-02-2022-0047. ISSN 0022-0418. Closed access icon / freely accessible version


    ToDo List

    Miscellaneous tasks

    Categories to look through

    (See also this much larger list of relevant articles without a lead image)

    Translation ToDo

    A list of related articles particularly good and notable enough to be worthy of a solid translation effort

    Requested articles (in general)

    1. ^ Backman, J. (2022). Radical conservatism and the Heideggerian right : Heidegger, de Benoist, Dugin. Frontiers in Political Science, 4, Article 941799. https://doi.org/10.3389/fpos.2022.941799

    Merging ToDo

    A list of related articles that may have resulted from a WP:POVFORK or may, at least, look like the functional equivalents of one
    Note that the exact target of a potential merge must not be provided here and that multiple options (e.g. generous use of Template:Excerpt) might accomplish the same