AI Content Conundrum: Sentiment Degradation Exacerbating the Misinformation Age

Fortunately being an amp8220AI sleuthamp8221 is relatively easy thanks to some emerging smart SaaS tools Such solutions will become crucial for those endeavoring to identify the use of AI in content creation dealing with challenges related to false positives in AI content detection discerning if AI material has yet been detected and even if AI amp8220distilledamp8221 content has been reconfigured to be more neutral in sentimentIn fact to that latter point of original content being watered down or repositioned to a more impartial format that does not accurately reflect the originatoramp8217s voice or purposeamp8212whether that be intended or unintended outcome of the spinamp8212one study has revealed how pervasive this is along with the implications related thereto This as Originalityaiamp8212a 99 accurate AI content plagiarism and fact checker SaaS assuring publishing with integrityamp8212released itsamp160findingsamp160that some of the most popular LLMs used to rewrite or paraphrase another text are making content more neutral in sentiment and in doing so are notably altering the nature and objective of the initial written workTo better understand and consider the possible consequences of such AIdriven content transformation relative to tone temperament and overall sentiment I connected with Originalityaiamp160Founder and CEO Jonathan Gillham for some insight into the study along with implications of the disconcerting findings it revealedMK Your study found that popular LLMs like OpenAIamp8217s ChatGPT Anthropicamp8217s Claude2 and Meta AIamp8217s Llama2 are officially making content more neutral in sentiment Why does this actually matterJG Employing LLMs to rewrite or paraphrase another text can certainly offer speed and ease in content production but it comes with caveats For example there might be a sound reason for coverage of a news event to have highly negative or positive sentiment Dampening those qualities might prevent readers from perceiving how potentially troublesome or heartening an event might be Outside of news content publishers might desire to convey a particular kind of sentiment to evoke feelings in readers and a neutralscoring story might struggle to do so On the other hand there could be uses for making texts with more neutral sentiment that read more like amp8220just the factsamp8221 Publishers might want to consider the tone and purpose of a piece and know that LLMs might modify texts in ways that affect those goalsMK What exactly is sentiment analysis which was the benchmark employed for your AI Paraphrased Content studyJG Sentiment analysis is the process of analyzing and categorizing texts as positive neutral or negative and to what degree Itamp8217s often used to assess opinions and feelings expressed in reviews or openended questions in surveys Many of the stories in this study had sentiment made more neutral after generative AI rewrote them In the Sentiment Analysis scale 1 is highly negative 5 is highly positive and 3 is neutral LLMs tended to move a storyamp8217s sentiment closer to 3 whether the original writing was more negative or positive In the aggregate the rewritten articles had their sentiment flattenedMK How was the study methodology undertaken and what were some of the specific key findings and data pointsJG We analyzed 100 articles for their sentiment or how positive or negative they were and then had them rewritten by three Large Language Models OpenAIamp8217s ChatGPT Anthropicamp8217s Claude2 and Meta AIamp8217s Llama2 The new textsamp8217 sentiment scores were then analyzed for any changes The 100 articles utilized in the study each from popular websites were rated by SapienIOamp8217s Sentiment Analysis for how positive neutral or negative each was We then had three different LLMsamp8212ChatGPT Claude2 and Llama2 specificallyamp8212each paraphrase the article and then analyze the sentiment of the new text These ratings were then compared to the original articleamp8217s sentiment rating The score given along with each rewritten articleamp8217s word count was analyzed for any relationshipsThe key findings include the substantiation that LLM rewrites moved the sentiment scores closer to the middle neutral part of the scale and that the resulting sentiment scores differed by LLM Llama2 had the most positive orientation scores with Claude2 having the most negative Itamp8217s also important to note that rewritten articles were made shorter than the original which could be part of the reason sentiment scores changedOverall the analysis showed no more than half a point in difference between the original articlesamp8217 average Sentiment Analysis score of 254 slightly more negative than neutral and the LLMsamp8217 rewrite averages of 272 Claude2 295 ChatGPT and 308 Llama2 However those differences became pronounced when looking at articles that originally held sentiment scores of 1 or 5 In those cases the rewrites differed by more than a point and up to 15 points on average pulling toward a neutral 3 If the original scored a 1 the rewrites averaged 235 When the original was a 5 the rewrites averaged 356MK You mentioned that the LLM rewrites often resulted in fewer words and that this could have impacted the results Can you elaborate a bit on thatJG Yes a possible explanation for the neutralization in sentiment could be that all three LLMs reduced the number of words when they rewrote articles Claude2 reduced words by a notable 435 compared to 135 for ChatGPT and 156 for Llama2 While shortening an article can be desirable for some purposes the reduction might eliminate details or potent phrases that indicate how negative or positive the sentiment of the story is Losing those details or descriptive words could explain part of the movement toward a rating of 3 neutral for stories with either the most positive or negative sentimentThis study was small but the data displayed suggests a slightly positive correlation between sentiment scores and word counts with longer texts receiving higher scores The trend was highlighted by comparing the three LLMs to each other Across all levels of sentiment in the original articles Claude2 consistently had both the lowest sentiment scores and the lowest word count while Llama2 had the highest sentiment scores and highest word countMK How pervasive do you feel the issue of AIgenerated content is becomingJG Some of our other research has looked at large companies that dominate Google search results These include Valnet Arena Group Cande Nastamp160 Red Ventures and DotDashMeredith among others Our analysis shows that 46 of major publishers are using AI for content creation This includes seven out of the 16 companies weamp8217ve studied with significant AI content detected on their sites On one particular globally popular sports magazine site an article was identified by Futurism as being penned by a amp8220fakeamp8221 author which our algorithm substantiated This is a tactic being increasingly used for a content marketing strategy called amp8220parasite SEOamp8221 where reputable domains host contentamp8212even wholly AI createdamp8212primarily for search engine ranking purposes Of course for readers of such content expecting a human was at the helm this kind of gratuitous and unimpassioned information propagation especially that with extreme reach can be dishearteningAccording to Axios amp8220By some estimates AIgenerated content could soon account for 99 or more of all information on the internet further straining already overwhelmed content moderation systemsamp8230Dozens of amp8216news sitesamp8217 filled with machine generated content of dubious quality have already cropped up with far more likely to followamp8212and some media sites are helping blur the linesamp8221University of Washington professor Kate Starbird an expert in the field told Axios that generative AI will deepen the misinformation problem in numerous ways amp8220Generative AI has the potential to accelerate the spread of both mis and disinformation and exacerbate the ongoing challenge of finding information we can trust onlineamp8221 she saidWith other reports indicating that a vast majorityamp8212over 75amp8212of consumers are reportedly concerned about AIdriven misinformation content sentiment degradation is emerging as an insidious facet of that overarching threat Although it is a more stealth and less considered offense the restructuring of content in a way that mitigates or outright eliminates the intended emotional tonalityamp8212the humanityamp8212of a text is exacerbating the Misinformation Age of AISourcehttpswwwaxioscom20230710aimisinformationresponsemeasuresPosted at MediaVillage through the Thought Leadership selfpublishing platformClick the social buttons to share this story with colleagues and friendsThe opinions expressed here are the author39s views and do not necessarily represent the views of MediaVillageorgMyersBizNet

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AI Content Conundrum: Sentiment Degradation Exacerbating the Misinformation Age

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Merilee Kern

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