A Woman Tracked Her Boyfriend’s Location to a Movie Theater, Found Him With Another Woman, and Threw Their Food on Them — X Is Divided ⁠38%

By Sayantan⁠61%

8/5/2026, 5:53:09 AM

BS Summary: This article contains 25 faulty reasoning types, including Confirmation Bias, Pessimism Bias, and False Dilemma, with Negativity Bias as the most egregious example at 23% saturation with 125 hits. Analysis detected 898 faulty-reasoning hits from 544 analyzed words, generating a BS Score of 37.1% and a BS Rank of ⁠38% (18,309 of 29,508 articles). This article is better (less manipulative) than 62.00% of the article peer group.

An X post from @IAmLilRico shared a video from TikTok account @driaaa.a showing a woman buying a movie ticket after noticing her boyfriend’s phone location was showing at a theater, despite him claiming he was going bowling with his friends. 
The video, shot in shaky point-of-view style, shows her walking through multiple dark theater rooms using her phone’s flashlight to search for him. 
The TikToker captions the video, “POV: he said he was going bowling with his hbs….. why we at the movies boo?” 
@driaaa.a Ughhh trust no ?? 
#fypシ #nationalgfsday #cheater #lame #mywhy 
♬ Happening Now – Romeo 
She eventually finds her boyfriend seated with another woman, then physically confronts both of them and throws their food on top of them, which appears to land on other moviegoers seated nearby. 
TikToker @driaaa.a account is known for posting scripted videos, pranks, and comedy content, and it is not confirmed whether this specific video depicts a real event or a staged scenario. 
Neither the boyfriend, the other woman, nor the original poster is identified by name in the clip or the post. 
If the confrontation shown is real, throwing food or making physical contact with strangers in a public venue could carry legal consequences separate from any dispute between the couple, though no theater, police department, or other named source has commented on the video. 
Several commenters framed the reaction as proof of devotion rather than cause for concern, writing approvingly of her response. 
One commenter wrote, “If your girl not ready to do some psycho sh-t like this then she don’t love you.” 
Others argued the situation pointed to a red flag in the relationship. 
One person wrote, “Having to share your location was already a sign she was crazy and got [sic] nothing to do with her time.” 
If your girl not ready to do some psycho shit like this then she dont love you ? 
- Tito Palmer (@titopalmerjr) August 4, 2026 
Some focused on how the TikToker walked through the corridors while tracking him down. 
One commenter wrote, “The dedication is crazy, the flashlight is crazier. 
She was really determined.” 
Others were more concerned about the chaos she caused on bystanders and theater staff not involved in the incident. 
They wrote, “Okay but she got it all over this innocent person next to them as well, and the workers are gonna be the ones having to clean all of that up. 
She could’ve just confronted him without making a mess and being disrespectful to others.” 
The Daily Dot was unable to independently verify whether this video depicts a real event or scripted content, as the account that posted it is known for both. 
The identities of everyone shown have not been confirmed, and no theater or law enforcement statement regarding the incident was found. 
Sign up to receive the Daily Dot’s Internet Insider newsletter for urgent news from the frontline of online. 
The post A Woman Tracked Her Boyfriend’s Location to a Movie Theater, Found Him With Another Woman, and Threw Their Food on Them — X Is Divided appeared first on The Daily Dot . 
Article reasoning-pattern comparisonThis article: 13.4%Sayantan: 4.8%dailydot.com: 3.5%Confirmation Bias13.4%This article: 4.4%Sayantan: 1.4%dailydot.com: 0.8%Anchoring Bias4.4%This article: 7.4%Sayantan: 4.5%dailydot.com: 3.5%Availability Heuristic7.4%This article: 0.0%Sayantan: 0.6%dailydot.com: 1.0%Representativeness Heuristic0.0%This article: 0.0%Sayantan: 0.9%dailydot.com: 0.6%Hindsight Bias0.0%This article: 5.5%Sayantan: 2.2%dailydot.com: 1.3%Overconfidence Bias5.5%This article: 5.5%Sayantan: 3.1%dailydot.com: 4.1%Framing Effect5.5%This article: 0.0%Sayantan: 0.7%dailydot.com: 0.5%Loss Aversion0.0%This article: 0.0%Sayantan: 0.1%dailydot.com: 0.5%Status Quo Bias0.0%This article: 0.0%Sayantan: 0.2%dailydot.com: 0.2%Sunk Cost Effect0.0%This article: 2.6%Sayantan: 0.7%dailydot.com: 1.1%Optimism Bias2.6%This article: 11.8%Sayantan: 1.6%dailydot.com: 1.1%Pessimism Bias11.8%This article: 23.0%Sayantan: 7.8%dailydot.com: 7.5%Negativity Bias23.0%This article: 3.5%Sayantan: 0.6%dailydot.com: 0.9%Self-Serving Bias3.5%This article: 3.5%Sayantan: 1.2%dailydot.com: 2.2%Fundamental Attribution Error3.5%This article: 0.0%Sayantan: 0.1%dailydot.com: 0.2%Actor-Observer Bias0.0%This article: 0.0%Sayantan: 2.0%dailydot.com: 1.2%In-Group Bias0.0%This article: 0.0%Sayantan: 1.5%dailydot.com: 1.1%Out-Group Homogeneity Bias0.0%This article: 2.0%Sayantan: 0.4%dailydot.com: 1.8%Halo Effect2.0%This article: 0.0%Sayantan: 0.3%dailydot.com: 0.3%Horn Effect0.0%This article: 0.0%Sayantan: 0.0%dailydot.com: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Sayantan: 0.5%dailydot.com: 0.6%Recency Bias0.0%This article: 2.6%Sayantan: 0.3%dailydot.com: 0.3%Primacy Effect2.6%This article: 0.0%Sayantan: 0.1%dailydot.com: 0.1%Blind-Spot Bias0.0%This article: 0.0%Sayantan: 1.9%dailydot.com: 1.7%Ad Hominem0.0%This article: 0.0%Sayantan: 0.1%dailydot.com: 0.3%Straw Man0.0%This article: 0.0%Sayantan: 2.5%dailydot.com: 1.6%Appeal to Authority0.0%This article: 11.4%Sayantan: 2.1%dailydot.com: 1.8%False Dilemma11.4%This article: 0.0%Sayantan: 0.4%dailydot.com: 0.6%Slippery Slope0.0%This article: 0.0%Sayantan: 0.1%dailydot.com: 0.1%Circular Reasoning0.0%This article: 9.0%Sayantan: 12.5%dailydot.com: 7.6%Hasty Generalization9.0%This article: 0.0%Sayantan: 0.3%dailydot.com: 0.2%Red Herring0.0%This article: 0.0%Sayantan: 0.7%dailydot.com: 2.0%Bandwagon0.0%This article: 5.7%Sayantan: 3.8%dailydot.com: 5.7%Appeal to Emotion5.7%This article: 8.3%Sayantan: 0.6%dailydot.com: 0.8%Begging the Question8.3%This article: 0.0%Sayantan: 1.2%dailydot.com: 1.2%Post Hoc (False Cause)0.0%This article: 0.0%Sayantan: 0.1%dailydot.com: 0.2%Tu Quoque0.0%This article: 7.9%Sayantan: 2.7%dailydot.com: 1.5%Burden of Proof7.9%This article: 0.0%Sayantan: 0.3%dailydot.com: 0.1%Appeal to Nature0.0%This article: 5.9%Sayantan: 0.2%dailydot.com: 0.2%Composition/Division5.9%This article: 8.1%Sayantan: 10.8%dailydot.com: 6.5%Anecdotal8.1%This article: 0.0%Sayantan: 0.3%dailydot.com: 0.1%No True Scotsman0.0%This article: 0.9%Sayantan: 1.3%dailydot.com: 1.6%Ambiguity (Equivocation)0.9%This article: 0.0%Sayantan: 0.0%dailydot.com: 0.0%Gambler’s Fallacy0.0%This article: 2.6%Sayantan: 0.1%dailydot.com: 0.2%Middle Ground2.6%This article: 0.0%Sayantan: 0.2%dailydot.com: 0.2%Personal Incredulity0.0%This article: 0.0%Sayantan: 0.1%dailydot.com: 0.1%Special Pleading0.0%This article: 5.5%Sayantan: 0.3%dailydot.com: 0.1%Genetic Fallacy5.5%This article: 7.5%Sayantan: 5.5%dailydot.com: 4.2%Unattributed Quote7.5%This article: 3.9%Sayantan: 2.4%dailydot.com: 2.7%Quote-first Misdirection3.9%This article: 0.0%Sayantan: 1.9%dailydot.com: 3.4%Biased Writer Voice0.0%This article: 0.0%Sayantan: 0.5%dailydot.com: 1.4%Indoctrination0.0%This article: 0.0%Sayantan: 0.1%dailydot.com: 0.1%Politically Left Leaning Bias0.0%This article: 0.0%Sayantan: 0.5%dailydot.com: 0.6%Politically Right Leaning Bias0.0%This article: 3.3%Sayantan: 1.1%dailydot.com: 1.6%Attempt to Sell a Product or S…3.3%

544 words analyzed.

Speakers

2speakers6.1%attributed speech511writer words
Selected voice

@driaaa.a

100%flagged-word coverage
26 attributed words79% of attributed speech74% writer coverage
0%42.5%85.0%Quote-first Misdirection+80.8 ptsWriter: 0.0%@driaaa.a: 80.8%80.8%Unattributed Quote+76.9 ptsWriter: 3.9%@driaaa.a: 80.8%80.8%Attempt to Sell a Product -3.5 ptsWriter: 3.5%@driaaa.a: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.