Oppositional thinking analysis: Conspiracy theories vs critical thinking narratives

Sponsored by
Symanto Research

Synopsis

Conspiracy theories are complex narratives that attempt to explain the ultimate causes of significant events as cover plots orchestrated by secret, powerful, and malicious groups [1]. A challenging aspect of identifying conspiracy with NLP models [2] stems from the difficulty of distinguishing critical thinking from conspiratorial thinking in automatic content moderation. This distinction is vital because labeling a message as conspiratorial when it is only oppositional could drive those who were simply asking questions into the arms of the conspiracy communities.

At PAN 2024 we aim at analyzing texts that reflect oppositional thinking and contain either conspiracy or critical narratives. The task will address two new challenges for the NLP research community: (1) to distinguish the conspiracy narrative from other oppositional narratives that do not express a conspiracy mentality (i.e., critical thinking); and (2) to identify in online messages the key elements of a narrative that fuels the intergroup conflict in oppositional thinking. To this end we provide two text corpora, one English and one Spanish, and we propose two sub-tasks:

1. Distinguishing between critical and conspiracy texts (subtask 1):

A binary classification task differentiating between (1) critical messages that question major decisions in the public health domain, but do not promote a conspiracist mentality; and (2) messages that view the pandemic or public health decisions as a result of a malevolent conspiracy by secret, influential groups.

  • Input: Set of texts, each associated with one of the two categories: CONSPIRACY, CRITICAL
  • Official evaluation metric: MCC [3]
  • Baselines: BERT classifier [4]

2. Detecting elements of the oppositional narratives (subtask 2):

A token-level classification task aimed at recognizing text spans corresponding to the key elements of oppositional narratives. Since conspiracy narratives are a special kind of causal explanation, we developed a span-level annotation scheme that identifies the goals, effects, agents, and the groups-in conflict in these narratives.

  • Input: Set of texts, each text accompanied with a (possibly empty) list of span annotations. Each annotation corresponds to a narrative element, and is described by its borders (start and end characters), and its category. There are six distinct span categories: AGENT, FACILITATOR, VICTIM, CAMPAIGNER, OBJECTIVE, NEGATIVE_EFFECT
  • Official evaluation metric: macro-averaged span-F1 [5]
  • Baseline: BERT-based multi-task token classifier (separate classification heads, common transformer backbone) [6]
GitHub repository with utilities, baselines, and additional instructions and guidelines can be found here

Task

Conspiracy Theories are complex narratives that attempt to explain the ultimate causes of significant events as cover plots orchestrated by secret, powerful, and malicious groups [1]. Automatic detection of CTs in text has recently gained popularity [2, 7, 8, 9]. The problem is commonly framed as binary classification, with fine-grained approaches corresponding to multi-label or multi-class classification. Two recent MediaEval challenges [8, 9] of coarse- and fine-grained classification of conspiratorial text [8, 9] led to a number of approaches demonstrating that the state-of-art architecture is a multi-task classifier [10, 11, 12] based on domain-specific CT-BERT model [12]. An LLM-based approach was also attempted [13].

However, existing approaches do not distinguish between critical and conspiratorial thinking. This distinction is important because labeling a text as conspiratorial when it is, in fact, oppositional to mainstream views, could potentially lead those who were simply asking questions closer to conspiracy communities. As several authors from the field of social sciences suggest, a fully-fledged conspiratorial worldview is the final step in a progressive "spiritual journey" that sets out by questioning social and political orthodoxies [1, 14, 15]. Additionally, recent research [16] has shown that the level of interaction with conspiracist users is the most important feature for predicting whether or not users join conspiracy communities. These insights have an important implication for automatic content moderation: if models do not differentiate between critical and conspiratorial thinking, there is a high risk of pushing people toward conspiracy communities.

Another important gap in the computational analysis of conspiratorial texts fails to address is the role that intergroup conflict (IGC) [17] plays in these narratives. Intergroup conflict is a way of framing events by emphasizing the hostility between groups, typically by using "us versus them" narrative, and by fueling the perceived injustice and threat to the group. The increasing potentially violent involvement of conspiracist communities in political processes suggests that one of the purposes of CTs is to enforce IGC and coordinate action [18]. Therefore, tools that enable an IGC-based analysis of conspiratorial texts could offer valuable insights for content moderation.

Motivated by the described issues, we propose a novel annotation scheme that distinguishes between conspiracy and critical texts, and defines important categories of oppositional narrative. In addition to the standard elements of conspiracy narratives such as agents (conspirators) and victims, the proposed scheme identifies the following categories: “facilitators” (collaborators of the agents, such as the media) and “campaigners” (those that unmask the conspiracy agenda). These types of actors are “key players” in IGC: the facilitators are tangible targets with whom real conflict is possible (in contrast to abstract agents such as secret groups), and the campaigners are those that show their opposition to the facilitators and try to persuade the victims to join their cause.

We focus on oppositional texts from the Telegram platform related to the COVID-19 pandemic, and construct English and Spanish corpora annotated with the described labeling schemes. This enables the NLP community to tackle two new tasks related to the two previously described phenomena: the binary classification task of distinguishing between conspiratorial and critical texts, and the task of detecting the elements of the oppositional narrative.

Award

We are pleased to announce that the best-performing team at the 11th International Competition on Author Profiling will be awarded 300,- Euro sponsored by Symanto.

Data [download]

The participants will work with a JSON file that contains all the texts in the train dataset, and their annotations. Each text corresponds to a dictionary that contains the ID, tokenized text, the binary category, and the span annotations. Span annotations are a list of dictionaries, each corresponding to an annotated span and containing the span’s category and text, designated by the start and end characters. This is an example of a JSON dictionary corresponding to a single, fully annotated, text:

                 {
                	"id": "91221",
                	"text": "\" Scientism backed ... not \" \" science - backed \" \" . There is nothing scientific about the Covid or childhood vaccine quackery that is slowly but surely killing , maiming and neurologically injuring the next generation — just as the Clintons and other Moloch worshippers want . \" ",
                	"category": "CONSPIRACY",
                	"annotations": [
                  	{
                    	"span_text": "that is slowly but surely killing , maiming and neurologically injuring the next generation",
                    	"category": "NEGATIVE_EFFECT",
                    	"start_char": 128,
                    	"end_char": 219,
                  	},
                  	{
                    	"span_text": "the next generation",
                    	"category": "VICTIM",
                    	"start_char": 200,
                    	"end_char": 219,
                  	},
                  	{
                    	"span_text": "the Clintons",
                    	"category": "AGENT",
                    	"start_char": 230,
                    	"end_char": 242,
                  	},
                  	{
                    	"span_text": "other Moloch worshippers",
                    	"category": "AGENT",
                    	"start_char": 247,
                    	"end_char": 271,
                  	}
                	]
                  }

            

At test time, the participants will receive text data in the above format. For each text, only the “id” and the “text” fields will be provided. The required output is a JSON file in the same format, with each text designated with an ID, and annotated with the text category, and the annotations. More details about the data, and the data utilities can be found in the task’s github repository.

Submission

The test set has been released [download].

The submission must be a ZIP file with a folder named as your team. The ZIP file must contain a JSON file with each of the subtasks and languages you want to participate in. The name of each JSON file must follow the structure below:

[team]_[task]_[lang].json

For instance, for team "rangel", you will send a file whose name will be rangel.zip with the following structure and files:

            rangel/
            |----rangel_task1_en.json
            |----rangel_task1_es.json
            |----rangel_task2_en.json
            |----rangel_task2_es.json
            

The content of the JSON file depends on the subtask:

Subtask 1:

                [
                  {
                    "id": "6742",
                    "category": "CRITICAL"
                  },
                  {
                    "id": "10596",
                    "category": "CONSPIRACY"
                  },
                ....
                ]
            

Subtask 2:

                [
                  {
                    "id": "6742",
                    "annotations": [
                      {
                        "start_char": 255,
                        "end_char": 360,
                        "category": "VICTIM"
                      },
                      {
                        "start_char": 89,
                        "end_char": 111,
                        "category": "VICTIM"
                      },
                      {
                        "start_char": 204,
                        "end_char": 271,
                        "category": "OBJECTIVE"
                      },
                      {
                        "start_char": 194,
                        "end_char": 275,
                        "category": "FACILITATOR"
                      }
                    ]
                  },
                ...
                ]
            

The ZIP file has to be sent to: kico (dot) rangel (at) gmail (dot) com

Please, use the following as a template for the title of the email where you substitute the placeholder for your team name and run number:

[PAN'24 - Oppositional Thinking Analysis] Evaluation of [team]'s run[1|2]

We will send you the obtained results as soon as we evaluate them, as well as we will contact you in case of technical errors.

IMPORTANT: Remember, you can submit up to two runs and we will keep the one with the highest score unless you tell us the contrary. In case of doubt, we will ask you to let us know which one you want to use for the final ranking.

Evaluation

The official evaluation metric for subtask 1 (critical vs. conspiracy classification) is MCC [3], while the official metric for subtask 2 (span-level detection of narrative elements) is macro-averaged span-F1 [5]. For subtask 1 we will also provide binary F1 scores for each of the classes, and for subtask 2 we will provide per-category span-F1 scores. For each of the task languages, English and Spanish, a separate ranking list will be maintained for each task.

We provide two hard baselines for these tasks. For subtask 1, the baseline is a standard BERT [4] classifier. For subtask 2, the baseline is a BERT-based multi-task token classifier (separate classification heads, common transformer backbone) [6]. The baselines are based on either English or Spanish BERT models, depending on the language. The task’s github repository contains the code of the baselines.

Official Ranking

TASK 1 - ENGLISH
POSITIONTEAMMCCF1-MACROF1-CONSPIRACYF1-CRITICAL
1IUCL0.83880.91940.89470.9441
2AI_Fusion0.83030.91470.88660.9429
3SINAI0.82970.91490.88860.9412
4ezio0.82120.90970.87920.9402
5hinlole0.81980.90980.88110.9386
6Zleon0.81950.90960.88040.9388
7virmel0.81920.90920.87930.9391
8inaki0.81490.90720.87700.9374
9yeste0.81240.90570.87460.9368
10auxR0.80880.90430.87390.9347
11Elias&Sergio0.80340.90120.86870.9338
12theateam0.80310.89990.86500.9347
13trustno10.79830.89910.86750.9307
14DSVS0.79700.89850.86740.9296
15sail0.79690.89780.86870.9268
16ojo-bes0.79690.89810.86480.9314
17RD-IA-FUN0.79650.89770.86360.9317
baseline-BERT0.79640.89750.86320.9318
18aish_team0.79170.89440.85800.9309
19rfenthusiasts0.79020.89480.86050.9291
20Dap_upv0.78980.89440.85930.9294
21oppositional_opposition0.78940.89350.85710.9300
22RD-IA-FUN0.78940.89470.86170.9276
23miqarn0.78810.89380.85930.9283
24CHEEXIST0.78750.89320.85760.9287
25tulbure0.78720.89170.85360.9297
26XplaiNLP0.78710.89220.85500.9294
27TheGymNerds0.78540.89230.85670.9278
28nlpln0.78440.89220.85800.9263
29RalloRico0.77710.88790.85590.9198
30LasGarcias0.77580.88550.84470.9263
31zhengqiaozeng0.77580.88660.84760.9256
32ALC_UPV_JD_20.77250.88600.84910.9230
33LorenaEloy0.77130.88470.84550.9239
34lnr-alhu0.77080.88530.84880.9219
35NACKO0.76920.88380.84460.9230
36paranoia-pulverizers0.76800.88380.84620.9215
37DiTana0.76530.88060.84900.9123
38FredYNed0.76430.88060.83920.9220
39dannuchihaxxx0.76430.88010.83770.9224
40lnr-detectives0.76310.88060.84720.9141
41TargaMarhuenda0.76170.88070.84240.9190
42Trainers0.75960.87970.84120.9182
43thetaylorswiftteam0.75770.87550.83020.9208
44locasporlnr0.75750.87870.83990.9174
45lnr-adri0.75520.87590.83260.9192
46TokoAI0.75420.87670.83630.9172
47ede0.75390.87690.83840.9155
48lnr-verdnav0.75290.87460.83080.9185
49lnr-dahe0.74880.87360.83080.9163
50epistemologos0.74860.87420.83410.9143
51lucia&ainhoa0.74730.87330.83160.9150
52pistacchio0.74140.86780.82000.9155
53lnr-BraulioPaula0.73930.86580.81650.9152
54Marc_Coral0.73920.86630.81760.9150
55Ramon&Cajal0.72840.86330.81690.9096
56lnr-lladrogal0.72530.86030.81060.9100
57lnr-fanny-nuria0.72530.85940.80820.9106
58MarcosJavi0.71900.85830.80970.9069
59lnr-cla0.71680.85730.80850.9061
60lnr-jacobantonio0.71680.85730.80850.9061
61MUCS0.71620.85380.79940.9082
62lnr-aina-julia0.71570.85740.81020.9046
63LaDolceVita0.70720.85190.80000.9037
64alopfer0.70560.85180.80120.9023
65lnr-luqrud0.70560.85180.80120.9023
66LNR-JoanPau0.70510.84260.77930.9058
67lnr-carla0.70000.84760.79320.9020
68lnr-Inetum0.69810.83280.76170.9039
69lnr-antonio0.68520.83000.75980.9002
70LluisJorge0.67840.83820.78300.8934
71anselmo-team0.67250.83410.77520.8930
72lnr-pavid0.59590.79740.72970.8651
73LNRMADME0.54690.77170.69140.8521
74lnr-mariagb_elenaog0.50690.72500.59660.8534
75LNR_080.44290.68340.52760.8391
76Kaprov0.37000.62400.42240.8255
77lnr_cebusqui0.04820.47600.18470.7674
78jtommor0.04030.51670.33120.7023
79eledu-0.45980.23500.27400.1960
80david-canet-0.63100.16320.18830.1381
81lnr-guilty-0.65950.14330.22470.0619
82lnrANRI-0.75510.10720.14740.0670
83ROCurve-0.80090.08840.11120.0656

TASK 1 - SPANISH
POSITIONTEAMMCCF1-MACROF1-CONSPIRACYF1-CRITICAL
1SINAI0.74290.87050.83190.9091
2auxR0.72050.85720.81120.9032
3RD-IA-FUN0.70280.84970.80350.8960
4Elias&Sergio0.69710.84850.80870.8884
5AI_Fusion0.68720.84190.79310.8908
6zhengqiaozeng0.68710.84170.79250.8909
7virmel0.68540.84260.80220.8831
8trustno10.68480.84000.78950.8906
9Zleon0.68260.84100.79550.8865
10ojo-bes0.68170.83950.80260.8764
11tulbure0.67220.82930.76990.8887
12sail0.67190.82990.77130.8884
13nlpln0.66810.83390.78720.8806
baseline-BERT0.66810.83390.78720.8806
14pistacchio0.66780.83270.78220.8833
15rfenthusiasts0.66560.82550.76430.8868
16XplaiNLP0.66220.82740.77080.8840
17yeste0.66090.82910.77700.8812
18oppositional_opposition0.66010.82740.77240.8825
19epistemologos0.65620.82640.77280.8801
20miqarn0.65620.82640.77280.8801
21theateam0.65570.82520.76950.8810
22ezio0.65350.82420.76830.8801
23lucia&ainhoa0.65240.82600.77650.8754
24TargaMarhuenda0.65160.82400.76920.8787
25TokoAI0.65160.82400.76920.8787
26paranoia-pulverizers0.64940.82460.77620.8730
27NACKO0.64670.82320.77390.8726
28ALC_UPV_JD_20.64670.82270.77050.8748
29DSVS0.64620.82310.77530.8709
30RD-IA-FUN0.64450.81600.75230.8796
31locasporlnr0.64370.82160.77090.8723
32DiTana0.63770.81870.76770.8696
33lnr-BraulioPaula0.63580.81730.77310.8615
34Dap_upv0.63060.81150.74930.8737
35TheGymNerds0.63060.81060.74700.8743
36MUCS0.62930.80600.73630.8756
37LasGarcias0.62470.81220.75940.8649
38lnr-dahe0.61960.80660.74370.8694
39lnr-adri0.61940.80600.74220.8698
40hinlole0.61920.80480.73910.8706
41RalloRico0.61050.80180.73700.8666
42lnr-aina-julia0.61030.79780.72640.8692
43lnr-verdnav0.61010.79910.72980.8684
44thetaylorswiftteam0.60660.80250.74360.8613
45lnr-alhu0.60240.79910.73580.8624
46lnr-luqrud0.60100.79450.72370.8654
47lnr-lladrogal0.59670.79420.72560.8627
48ede0.59650.79670.73410.8593
49Fred&Ned0.59310.79400.72830.8597
50LaDolceVita0.59210.78180.69810.8656
51LNR-JoanPau0.59200.79160.72180.8614
52anselmo-team0.58990.78600.70850.8634
53Ramon&Cajal0.58580.79160.72810.8552
54lnr-fanny-nuria0.58130.78740.71810.8567
55lnr-antonio0.57360.78160.70710.8561
56LluisJorge0.56900.77500.69290.8571
57lnr-cla0.56510.77880.70550.8520
58lnr-jacobantonio0.56510.77880.70550.8520
59lnr-pavid0.55690.77710.70890.8453
60alopfer0.55200.77270.69840.8470
61LNRMADME0.54900.77040.69370.8471
62lnr-carla0.54840.76860.68900.8482
63LorenaEloy0.54330.76210.67510.8492
64CHEEXIST0.53790.59950.56210.5456
65lnr-guilty0.52730.76200.68800.8360
66eledu0.50570.72630.60980.8429
67lnr-mariagb_elenaog0.49660.73250.62710.8379
68dannuchihaxxx0.47270.73100.63820.8238
69lnr-detectives0.40290.67340.65090.6960
70LNR_080.06080.47710.20000.7542
71jtommor0.01050.50510.38130.6288
72lnr-Inetum0.00000.38800.00000.7760
73Marc_Coral0.00000.26790.53590.0000
74MarcosJavi-0.03890.38870.00540.7720
75lnr_cebusqui-0.41120.24810.34660.1496
76david-canet-0.50580.21140.30290.1199
77lnrANRI-0.61460.17660.19390.1593
78ROCurve-0.64570.16280.17700.1485

TASK 2 - ENGLISH
POSITIONTEAMspan-F1span-Pspan-Rmicro-span-F1
1tulbure0.62790.58590.67900.6120
2Zleon0.60890.55370.68810.5856
3hinlole0.58860.52430.68340.5571
4oppositional_opposition0.58660.53470.65860.5344
5AI_Fusion0.58050.55850.60820.5437
6virmel0.57420.52350.64770.5540
7miqarn0.57390.51840.64620.5325
8TargaMarhuenda0.57010.51610.64770.5437
9ezio0.56940.52290.63400.5389
10zhengqiaozeng0.56660.51220.64850.5421
11Elias&Sergio0.56270.51490.63640.5248
12DSVS0.55980.53320.60120.5287
13CHEEXIST0.55240.47670.68450.5299
14rfenthusiasts0.54790.53810.56660.5408
15ALC_UPV_JD_20.53770.46430.65620.4956
baseline-BETO0.53230.46840.63340.4998
16Dap_upv0.52720.46170.62970.4973
17aish_team0.52130.41810.74560.2571
18SINAI0.45820.55530.42790.4571
19Trainers0.33820.51240.26090.2858
20nlpln0.33390.52860.33030.2710
21ROCurve0.29960.31540.30310.3425
22TokoAI0.27600.18700.61190.2677
23DiTana0.27560.52590.19470.2599
24TheGymNerds0.20700.20760.21270.2329
25epistemologos0.17090.12860.32440.1201
26theateam0.15030.14010.16520.0387
27LaDolceVita0.07260.20400.04530.0630
28kaprov0.01500.02610.01650.0600

TASK 2 - SPANISH
POSITIONTEAMspan-F1span-Pspan-Rmicro-span-F1
1tulbure0.61290.61590.61290.6108
2Zleon0.58750.54390.64740.5939
3AI_Fusion0.57770.54370.61890.5843
4CHEEXIST0.56210.53790.59950.5456
5virmel0.56160.49630.65840.5620
6miqarn0.56030.51170.62730.5618
7DSVS0.55290.53840.57850.5323
8TargaMarhuenda0.53640.51280.57100.5385
9Elias&Sergio0.51510.48640.55330.5231
10hinlole0.49940.45300.57400.4890
baseline-BETO0.49340.45330.56210.4952
11Dap_upv0.49140.45550.54740.4917
12zhengqiaozeng0.49030.45070.54940.4874
13ALC_UPV_JD_20.48850.45090.54580.4683
14ezio0.48690.46230.52290.4947
15nlpln0.46720.51740.44260.2961
16rfenthusiasts0.46660.51040.43410.4697
17SIANI0.41510.46300.40540.4781
18TheGymNerds0.39840.36210.44830.5024
19DiTana0.30040.44900.23620.3117
20ROCurve0.26490.27060.26270.3562
21TokoAI0.18780.11890.56590.1739
22epistemologos0.16570.19060.18640.1534
23LaDolceVita0.10560.11580.09750.1321
24theateam0.09940.10510.09620.0358
25oppositional_opposition0.00370.03490.00220.0014
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Important Dates

  • February 23, 2024: Train data release
  • May 30, 2024: Software submission deadline
  • June 15, 2024: Participant paper submission Midnight CEST
  • July 1st, 2024: Peer review notification
  • July 7th, 2024: Camera-ready participant papers submission Midnight CEST

Funds

XAI-DisInfodemics: eXplainable AI for disinformation and conspiracy detection during infodemics (PLEC2021-007681)

Project XAI-DisInfodemics: eXplainable AI for disinformation and conspiracy detection during infodemics (PLEC2021-007681) funded by MICIU/AEI/ 10.13039/501100011033 and by “European Union NextGenerationEU/PRTR”.

Task Committee