def METHOD_NAME(date, aliases):
    """Pull dark web mentions data for an organization."""
    METHOD_NAME = ""
    for mention in aliases:
        METHOD_NAME += '"' + mention + '"' + ","
    METHOD_NAME = METHOD_NAME[:-1]
    query = "site:forum_* AND date:" + date + " AND " + "(" + str(METHOD_NAME) + ")"
    LOGGER.info("Query:")
    LOGGER.info(query)
    count = 1
    while count < 7:
        try:
            LOGGER.info("Intel post try #%s", count)
            resp = intel_post(query, frm=0, scroll=False, result_size=1)
            break
        except Exception:
            LOGGER.info("Error. Trying intel_post again...")
            count += 1
            continue
    count_total = resp["total_intel_items"]
    LOGGER.info("Total Mentions: %s", count_total)
    i = 0
    all_mentions = []
    if count_total < 10000:
        while i < count_total:
            # Recommended "from" and "result_size" is 50. The maximum is 400.
            resp = intel_post(query, frm=i, scroll=False, result_size=200)
            i += 200
            LOGGER.info("Getting %s of %s....", i, count_total)
            intel_items = resp["intel_items"]
            df_mentions = pd.DataFrame.from_dict(intel_items)
            all_mentions.append(df_mentions)
            df_all_mentions = pd.concat(all_mentions).reset_index(drop=True)
    else:
        while i < count_total:
            # Recommended "from" and "result_size" is 50. The maximum is 400.
            resp = intel_post(query, frm=i, scroll=True, result_size=400)
            i += 400
            LOGGER.info("Getting %s of %s....", i, count_total)
            intel_items = resp["intel_items"]
            df_mentions = pd.DataFrame.from_dict(intel_items)
            all_mentions.append(df_mentions)
            df_all_mentions = pd.concat(all_mentions).reset_index(drop=True)
    return df_all_mentions